::faceted.search.hypothesis?any=sweet+spot

no need to move server with loss of network and even data

a clear case of a "better spot"

the dual of a sweat spot

one that is clearly bellow the threshold of what is required

again it should have said sweet spot

creative dislexia

five things together give you what u want

what is required just beyond the threshold the sweat spot

creative typo

of course it is sweet spot of Alan Kay

💻/thinkpad/🧊/me/📓/2026/02/09?t=local.first.software&extra=requirements

extended list of required green check marks

what is required above the threshold, the sweet spot

• Fast
• Multi-device 3
• Offline
• Collaboration
  • continuous without being synchronous
  • eventual consistency with full verifiable provenance
• recapitulable, summarisable history of co-evolution
• Longevity
• Privacy
• User control
• evergreen reproducibikity
• future compatibility

seven green checkmarks

what is required above the threshold, the sweet spot

• Fast
• Multi-device 3
• Offline
• Collaboration
• Longevity
• Privacy
• User control

Summary: Claude Usage Limits & Cost Analysis

Subscription vs. API Efficiency

• Massive Cost Savings: Claude subscriptions can be up to 36x cheaper than using the API for equivalent token throughput.
• The "Max 5x" Sweet Spot: The $100/mo Max 5x plan is identified as the most optimized tier, offering roughly 8.3x more weekly usage than the Pro plan (exceeding its "5x" marketing).
• The "Max 20x" Diminishing Returns: While the $200/mo tier provides 4x higher short-term (5-hour) burst limits than the Max 5x, its weekly ceiling is only ~2x higher, making it less efficient for consistent long-term work.

Dual-Layer Usage Framework

• 5-Hour Rolling Window: Controls "burst" activity. The counter starts at your first prompt; once reached, you must wait for the window to reset.
• 7-Day Weekly Ceiling: A hard cap on total "active compute hours" (time spent processing tokens/reasoning). This acts as a global safety valve for the system.
• Unified Quota: All usage across the browser (claude.ai), Claude Desktop, and Claude Code (terminal) counts toward the same unified limit.

Token Consumption Dynamics

• Exponential Context Cost: Claude re-reads the entire chat history for every new message. A 50-message thread uses significantly more tokens (and quota) than five separate 10-message chats.
• Input-Heavy Bias: Large file attachments, long project instructions, and extensive "Extended Thinking" sessions consume the quota much faster than short, text-only queries.

Optimization Strategies

• The /compact Command: Users are encouraged to use /compact (in Claude Code) or manually summarize/restart chats every 15–20 messages to reset the "token tax" of long histories.
• Lean Context: Keeping CLAUDE.md and project documentation concise prevents "context bloat" from draining limits prematurely.
• Strategic Timing: Since the 5-hour window starts with the first prompt, power users should time their first interaction to align with their most intensive coding blocks.

Recommendation: Which Claude Tier is Right for You?

1. The "Value King": Max 5x ($100/mo)

• Why it’s recommended: Data analysis shows this tier is "over-provisioned." While marketed as 5x, it often provides ~6x higher session limits and ~8.3x higher weekly limits than the Pro plan.
• The Sweet Spot: It offers the best balance between a massive increase in capacity and price. Most daily professional users and developers find it nearly impossible to hit these limits even with "all-day" use.
• Best For: Professional developers, heavy researchers, and those who want consistent access to the Claude 3.5 Opus model without hitting daily caps.

2. The "Sprint Specialist": Max 20x ($200/mo)

• The Caveat: Despite the name, the weekly ceiling is only ~2x higher than the Max 5x plan, not 4x higher. You are essentially paying for a much higher "burst" capacity.
• Why choose it: It allows for extremely high-intensity sessions (up to 900+ messages in 5 hours). This is useful if you do massive "sprints" where you need Claude to process huge amounts of data or code in a very short window.
• Best For: Solopreneurs building products in rapid bursts, or users who never want to think about rate limits during a 4–6 hour deep-work session.

3. The "Standard Choice": Pro ($20/mo)

• Why it’s recommended: It is 5x cheaper than the next tier and sufficient for 90% of users who use Claude for general writing, light coding, or occasional complex tasks.
• Best For: Students, casual coders, and users with small projects (under 1,000 lines of code).

Summary Table

• Quick Comparison Summary
  • Pro: $20/mo | Baseline Capacity | Best for personal/standard use.
  • Max 5x: $100/mo | ~8.3x Weekly Capacity | Best overall value for power users.
  • Max 20x: $200/mo | ~16x Weekly Capacity | Best for high-intensity "burst" work.

u/Koponewt aka Pelicram's advice for using fishing line to replace drawbands.

This is what I was arriving at in my last combat, where is the sweet spot of too much information and not enough information? A good alternative is to include a key with information on difficult words or phrases that may not be known to a newcomer or beginner in certain areas. Providing this key, will provide information for those who need it and simultaneously allow readers who are familiar to skip past it and not get 'annoyed' with the product or writing.

For the average office-type chair, the sweet spot you're looking for is a tabletop about 26 inches off of the floor.

Most modern desks have their tabletops at 28-29" off of the floor which is too high for comfortably typing on a typewriter. The larger old school typewriter desks (double and single pedestals) often had a flip top or spring loaded side compartment that brought the surface that the typewriter sat on down to a more comfortable 26" off of the floor. Similarly you'll see desk returns for these desks which are an inch or two shorter to allow for a typewriter off to the side. Many of the same tanker desks had writing drawers that pulled out to provide space over the other drawers for writing and these also make great surfaces for typewriters to be an inch or two lower than the standard height. These desks usually are heavy and take up some significant floor space.

From the 1920s, a variety of manufacturers made stand-alone typewriter stands, typically with two drop leaves on either side and wheels to give one easy space for their typewriter that didn't take up a huge footprint and could be moved around the office or home as needed. Similar to these in the modern furniture space, you might find a variety of side tables or occasional tables with tabletops at a more comfortable 26-27" for your typing.

For off-label use cases, you could try a counter-height stool (24-26") as a temporary typewriter stand to pair with most standard office chairs. Generally bar stools are much taller in the 36" range, so don't do this unless it's your intention to type standing up.

I've got a 20 drawer library card catalog with a tabletop height of 36.5" that makes an excellent height for a standing desk for typing.

reply to u/The-Wolf-Bandit at https://old.reddit.com/r/typewriters/comments/1q6e8oo/whatdoyouguyshaveyourtypewriters_on/

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In the well-written manuscript by Tarafder et al., the authors follow up on their previous investigations of the filamentous bacteriophage Pf4, which self-assembles into a crystalline droplet surrounding Pseudomonas aeruginosa cells within a biofilm. Using theoretical coarse-grained molecular dynamics (MD) simulations, they predict that binding a small molecule or protein to the surface of bacteriophage Pf4 should disrupt the attraction-in this case depletion attraction-between individual phage particles. To test this hypothesis, nanobodies were raised against Pf4, and two promising candidates, Nb43 and Nb-D11, were identified. These nanobodies were characterized using biochemical assays, and binding of Nb43 to CoaB, the major coat protein, was visualized using cryo-EM. Using fluorescence microscopy and cryo-ET, the authors convincingly demonstrate that nanobodies can disrupt Pf4 crystalline droplet formation. Strikingly, nanobody-mediated disruption of Pf4 droplets also increases antibiotic susceptibility of P. aeruginosa both in vitro and in biofilm settings.

Major comments

1) Theoretical modelling: The MD simulations, as currently presented, do not add conceptual depth to the study. The idea that blocking an interaction site between phages (whether through active-site interference, obstruction of a protein-protein interface, or simple steric hindrance) would prevent alignment is straightforward and does not necessarily require MD simulations to justify. As such, this section feels superfluous and is currently the weakest point in an otherwise strong manuscript. Unless the simulations can meaningfully address at least some of the questions listed below, the authors should consider removing this part:

The MD simulation is very simplistic, and filamentous phages are clearly not hard rods, as seen in the cryo-EM images. Would a certain degree of Pf4 flexibility allow to stabilize droplets even in the presence of low concentrations of Pf4 binders?

How do the MD simulations explain that already pre-formed crystalline droplets can be penetrated and disassembled by small Pf4 binders?

The authors state that Pf4 binders must be large relative to the depletant particles. Can this be demonstrated experimentally? Is there a sweet spot, as large molecules potentially cannot penetrate preformed droplets?

2) Nanobody penetration into crystalline droplets (Extended Data Fig. 6a-d) vs. antibiotic penetration (Fig. 4) The authors show that Nb43 penetrates Pf4 droplets even at concentrations that do not disrupt droplet stability. How do the authors explain that a relatively large nanobody penetrates the crystalline droplet, whereas a much smaller antibiotic does not diffuse trough the droplet?

In the experiments shown in Figure 4, the authors assess antibiotic activity against P. aeruginosa in the presence of Pf4 crystalline droplets. If I understand correctly, the additionally added Pf4 droplets do not physically encompass the bacteria, yet they still reduce antibiotic tolerance. If so, this appears to contradict the conclusion that Pf4 droplets act primarily as a diffusion barrier (as stated in the section title). Instead, this would suggest that Pf4 may reduce antibiotic potency through another mechanism (e.g., direct binding or sequestration). Would it be possible to test the addition of Pf4 alone, without the biopolymer alginate, to determine whether Pf4 itself is sufficient to increase antibiotic tolerance?

Minor comments:

• Title: The title is overstated. Please consider changing it to something similar to: "Targeted disruption of phage liquid crystalline droplets abolishes antibiotic tolerance in Pseudomonas aeruginosa biofilms."
• Introduction sentence: "...where filamentous phage particles align along their axis in the presence of biopolymer,..." Please introduce what biopolymers are and specify which types are relevant here.
• Amorphous Pf4 aggregates after Nb43 treatment (Fig 3b,e): The authors should discuss the nature of these aggregates. It appears that smaller spindles are both broken up and impeded in their formation after Nb43 treatment, whereas larger aggregates seem to persist.
• Fig. 3c and 3f: Please describe how liquid crystalline structures were defined in the fluorescence images. Were thresholds for size, intensity, or morphology applied?
• Use of P. aeruginosa ΔPAO728: For clarity, please explain why the strain lacking the Pf4 integrase is included in the in-vitro assays.

Discussion:

Neisseria meningitidis and Vibrio cholerae use filamentous phages to increase virulence. Do these phages also form liquid crystalline droplets? If not, how do the authors envision that the nanobody strategy described here could be applied to prevent infection? In general, the findings are hard to generalize to other biofilms matrices, which are highly heterogenous.

Significance

Bacterial biofilms and their associated antibiotic tolerance represent a major clinical burden, and new strategies to overcome these defenses are urgently needed. The strategy presented here-targeting and disrupting the protective extracellular matrix formed by liquid crystalline Pf4 phage droplets-is an exciting and innovative approach with clear translational potential for combating P. aeruginosa biofilms. The study is experimentally rigorous, well written, and carefully analyzed, and it represents a logical and impactful next step following the group's previous work. This manuscript will have significant impact on the field of P. aeruginosa biofilm research by providing a mechanistically grounded method to disrupt the protective biofilm architecture. However, it is important to note that the extracellular matrix architecture of biofilms formed by other bacterial species differs substantially, and thus the current findings cannot be directly generalized beyond P. aeruginosa without further investigation.

Cloud hosting can become an expensive abstraction layer quickly. I also think there's an entire generation of coders / engineers who treat silo'd cloudhosting as a given, without considering other options and their benefits. Large window for selfhosting in which postgres almost always falls

Instead of shipping early shipping often

or "Get IT Right First time"

Get the IT right first.

If you do that IT can self-host and self-realize the IT

as a Conversations

After all Software is a Symmathetic Conversaiton creating the Medium, the Sweet spot, just above the threshold that is trully required.

Proving that the "Worse IS Not Better"

Need the Jewel like Diamond approach

Reviewer #2 (Public review):

In the presented manuscript, Teplenin and colleagues use both electrical pacing and optogenetic stimulation to create a reproducible, controllable source of ectopy in cardiomyocyte monolayers. To accomplish this, they use a careful calibration of electrical pacing characteristics (i.e., frequency, number of pulses) and illumination characteristics (i.e., light intensity, surface area) to show that there exists a "sweet spot" where oscillatory excitations can emerge proximal to the optogenetically depolarized region following electrical pacing cessation, akin to pacemaker cells. Furthermore, the authors demonstrate that a high-frequency electrical wave-train can be used to terminate these oscillatory excitations. The authors observed this oscillatory phenomenon both in vitro (using neonatal rat ventricular cardiomyocyte monolayers) and in silico (using a computational action potential model of the same cell type). These are surprising findings and provide a novel approach for studying triggered activity in cardiac tissue.

The study is extremely thorough and one of the more memorable and grounded applications of cardiac optogenetics in the past decade. One of the benefits of the authors' "two-prong" approach of experimental preps and computational models is that they could probe the number of potential variable combinations much deeper than through in vitro experiments alone. The strong similarities between the real-life and computational findings suggest that these oscillatory excitations are consistent, reproducible, and controllable.

Triggered activity, which can lead to ventricular arrhythmias and cardiac sudden death, has been largely contributed to sub-cellular phenomena, such as early or delayed afterdepolarizations, and thus to date has largely been studied in isolated single cardiomyocytes. However, these findings have been difficult to translate to tissue- and organ-scale experiments, as well-coupled cardiac tissue has notably different electrical properties. This underscores the significance of the study's methodological advances: use of a constant depolarizing current in a subset of (illuminated) cells to reliably result in triggered activity could facilitate the more consistent evaluation of triggered activity at various scales. An experimental prep that is both repeatable and controllable (i.e., both initiated and terminated through the same means) is a boon for further inquiry.

The authors also substantially explored phase space and single cell analyses to document how this "hidden" bi-stable phenomenon can be uncovered during emergent collective tissue behavior. Calibration and testing of different aspects (e.g.: light intensity, illuminated surface area, electrical pulse frequency, electrical pulse count) and other deeper analyses, as illustrated in Figures S3-S8 and Video S1, are significant and commendable.

Given the study is computational, it is surprising that the authors did not replicate their findings using well-validated adult ventricular cardiomyocyte action potential models, such ten Tusscher 2006 or O'Hara 2011. This may have felt out-of-scope, given the nice alignment of rat cardiomyocyte data between in vitro and in silico experiments. However, it would have been helpful peace-of-mind validation, given the significant ionic current differences between neonatal rat and adult ventricular tissue. It is not fully clear whether the pulse trains could have resulted in the same bi-stable oscillatory behavior, given the longer APD of humans relative to rats. The observed phenomenon certainly would be frequency-dependent and would have required tedious calibration for a new cell type, albeit partially mitigated by the relative ease of in silico experiments.

There are likely also mechanistic differences between this optogenetically-tied oscillatory behavior and triggered activity observed in other studies. This is because the constant light-elicited depolarizing current is disrupting the typical resting cardiomyocyte state, thereby altering the balance between depolarizing ionic currents (such as Na+ and Ca2+) and repolarizing ionic currents (such as K+ and Ca2+). The oscillatory excitations appear to later emerge at the border of the illuminated region and non-stimulated surrounding tissue, which is likely an area of high source-sink mismatch. The authors appear to acknowledge differences in this oscillatory behavior and previous sub-cellular triggered activity research in their discussion of ectopic pacemaker activity, which are canonically observed in genetic, pharmacologic, or pathological ionic conditions. Regardless, it is exciting to see new ground being broken in this difficult-to-characterize experimental space, even if the method illustrated here may not necessarily be broadly applicable.

Comments on revisions:

I have read the authors' rebuttal to our earlier comments and do not have any further questions or comments. Thank you for implementing the minor improvements to Figure visualizations and for creating Video S1 to accompany the article.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Teplenin and coworkers assesses the combined effects of localized depolarization and excitatory electrical stimulation in myocardial monolayers. They study the electrophysiological behaviour of cultured neonatal rat ventricular cardiomyocytes expressing the light-gated cation channel Cheriff, allowing them to induce local depolarization of varying area and amplitude, the latter titrated by the applied light intensity. In addition, they used computational modeling to screen for critical parameters determining state transitions and to dissect the underlying mechanisms. Two stable states, thus bistability, could be induced upon local depolarization and electrical stimulation, one state characterized by a constant membrane voltage and a second, spontaneously firing, thus oscillatory state. The resulting 'state' of the monolayer was dependent on the duration and frequency of electrical stimuli, as well as the size of the illuminated area and the applied light intensity, determining the degree of depolarization as well as the steepness of the local voltage gradient. In addition to the induction of oscillatory behaviour, they also tested frequency-dependent termination of induced oscillations.

Strengths:

The data from optogenetic experiments and computational modelling provide quantitative insights into the parameter space determining the induction of spontaneous excitation in the monolayer. The most important findings can also be reproduced using a strongly reduced computational model, suggesting that the observed phenomena might be more generally applicable.

Weaknesses:

While the study is thoroughly performed and provides interesting mechanistic insights into scenarios of ventricular arrhythmogenesis in the presence of localized depolarized tissue areas, the translational perspective of the study remains relatively vague. In addition, the chosen theoretical approach and the way the data are presented might make it difficult for the wider community of cardiac researchers to understand the significance of the study.

Reviewer #2 (Public review):

In the presented manuscript, Teplenin and colleagues use both electrical pacing and optogenetic stimulation to create a reproducible, controllable source of ectopy in cardiomyocyte monolayers. To accomplish this, they use a careful calibration of electrical pacing characteristics (i.e., frequency, number of pulses) and illumination characteristics (i.e., light intensity, surface area) to show that there exists a "sweet spot" where oscillatory excitations can emerge proximal to the optogenetically depolarized region following electrical pacing cessation, akin to pacemaker cells. Furthermore, the authors demonstrate that a high-frequency electrical wave-train can be used to terminate these oscillatory excitations. The authors observed this oscillatory phenomenon both in vitro (using neonatal rat ventricular cardiomyocyte monolayers) and in silico (using a computational action potential model of the same cell type). These are surprising findings and provide a novel approach for studying triggered activity in cardiac tissue.

The study is extremely thorough and one of the more memorable and grounded applications of cardiac optogenetics in the past decade. One of the benefits of the authors' "two-prong" approach of experimental preps and computational models is that they could probe the number of potential variable combinations much deeper than through in vitro experiments alone. The strong similarities between the real-life and computational findings suggest that these oscillatory excitations are consistent, reproducible, and controllable.

Triggered activity, which can lead to ventricular arrhythmias and cardiac sudden death, has been largely attributed to sub-cellular phenomena, such as early or delayed afterdepolarizations, and thus to date has largely been studied in isolated single cardiomyocytes. However, these findings have been difficult to translate to tissue and organ-scale experiments, as well-coupled cardiac tissue has notably different electrical properties. This underscores the significance of the study's methodological advances: the use of a constant depolarizing current in a subset of (illuminated) cells to reliably result in triggered activity could facilitate the more consistent evaluation of triggered activity at various scales. An experimental prep that is both repeatable and controllable (i.e., both initiated and terminated through the same means).

The authors also substantially explored phase space and single-cell analyses to document how this "hidden" bi-stable phenomenon can be uncovered during emergent collective tissue behavior. Calibration and testing of different aspects (e.g., light intensity, illuminated surface area, electrical pulse frequency, electrical pulse count) and other deeper analyses, as illustrated in Appendix 2, Figures 3-8, are significant and commendable.

Given that the study is computational, it is surprising that the authors did not replicate their findings using well-validated adult ventricular cardiomyocyte action potential models, such as ten Tusscher 2006 or O'Hara 2011. This may have felt out of scope, given the nice alignment of rat cardiomyocyte data between in vitro and in silico experiments. However, it would have been helpful peace-of-mind validation, given the significant ionic current differences between neonatal rat and adult ventricular tissue. It is not fully clear whether the pulse trains could have resulted in the same bi-stable oscillatory behavior, given the longer APD of humans relative to rats. The observed phenomenon certainly would be frequency-dependent and would have required tedious calibration for a new cell type, albeit partially mitigated by the relative ease of in silico experiments.

For all its strengths, there are likely significant mechanistic differences between this optogenetically tied oscillatory behavior and triggered activity observed in other studies. This is because the constant light-elicited depolarizing current is disrupting the typical resting cardiomyocyte state, thereby altering the balance between depolarizing ionic currents (such as Na+ and Ca2+) and repolarizing ionic currents (such as K+ and Ca2+). The oscillatory excitations appear to later emerge at the border of the illuminated region and non-stimulated surrounding tissue, which is likely an area of high source-sink mismatch. The authors appear to acknowledge differences in this oscillatory behavior and previous sub-cellular triggered activity research in their discussion of ectopic pacemaker activity, which is canonically expected more so from genetic or pathological conditions. Regardless, it is exciting to see new ground being broken in this difficult-to-characterize experimental space, even if the method illustrated here may not necessarily be broadly applicable.

We thank the reviewers for their thoughtful and constructive feedback, as well as for recognizing the conceptual and technical strengths of our work. We are especially pleased that our integrated use of optogenetics, electrical pacing, and computational modelling was seen as a rigorous and innovative approach to investigating spontaneous excitability in cardiac tissue.

At the core of our study was the decision to focus exclusively on neonatal rat ventricular cardiomyocytes. This ensured a tightly controlled and consistent environment across experimental and computational settings, allowing for direct comparison and deeper mechanistic insight. While extending our findings to adult or human cardiomyocytes would enhance translational relevance, such efforts are complicated by the distinct ionic properties and action potential dynamics of these cells, as also noted by Reviewer #2. For this foundational study, we chose to prioritize depth and clarity over breadth.

Our computational domain was designed to faithfully reflect the experimental system. The strong agreement between both domains is encouraging and supports the robustness of our framework. Although some degree of theoretical abstraction was necessary (thereby sometimes making it a bit harder to read), it reflects the intrinsic complexity of the collective behaviours we aimed to capture such as emergent bi-stability. To make these ideas more accessible, we included simplified illustrations, a reduced model, and extensive supplementary material.

A key insight from our work is the emergence of oscillatory behaviour through interaction of illuminated and non-illuminated regions. Rather than replicating classical sub-cellular triggered activity, this behaviour arises from systems-level dynamics shaped by the imposed depolarizing current and surrounding electrotonic environment. By tuning illumination and local pacing parameters, we could reproducibly induce and suppress these oscillations, thereby providing a controllable platform to study ectopy as a manifestation of spatial heterogeneity and collective dynamics.

Altogether, our aim was to build a clear and versatile model system for investigating how spatial structure and pacing influence the conditions under which bistability becomes apparent in cardiac tissue. We believe this platform lays strong groundwork for future extensions into more physiologically and clinically relevant contexts.

In revising the manuscript, we carefully addressed all points raised by the reviewers. We have also responded to each of their specific comments in detail, which are provided below.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the authors):

Please find my specific comments and suggestions below:

(1) Line 64: When first introduced, the concept of 'emergent bi-stability' may not be clear to the reader.

We concur that the full breadth of the concept of emergent bi-stability may not be immediately clear upon first mention. Nonetheless, its components have been introduced separately: “emergent” was linked to multicellular behaviour in line 63, while “bi-stability” was described in detail in lines 39–56. We therefore believe that readers could form an intuitive understanding of the combined term, which will be further clarified as the manuscript develops. To further ease comprehension of the reader, we have added the following clarification to line 64:

“Within this dynamic system of cardiomyocytes, we investigated emergent bi-stability (a concept that will be explained more thoroughly later on) in cell monolayers under the influence of spatial depolarization patterns.”

(2) Lines 67-80: While the introduction until line 66 is extremely well written, the introduction of both cardiac arrhythmia and cardiac optogenetics could be improved. It is especially surprising that miniSOG is first mentioned as a tool for optogenetic depolarisation of cardiomyocytes, as the authors would probably agree that Channelrhodopsins are by far the most commonly applied tools for optogenetic depolarisation (please also refer to the literature by others in this respect). In addition, miniSOG has side effects other than depolarisation, and thus cannot be the tool of choice when not directly studying the effects of oxidative stress or damage.

The reviewer is absolutely correct in noting that channelrhodopsins are the most commonly applied tools for optogenetic depolarisation. We introduced miniSOG primarily for historical context: the effects of specific depolarization patterns on collective pacemaker activity were first observed with this tool (Teplenin et al., 2018). In that paper, we also reported ultralong action potentials, occurring as a side effect of cumulative miniSOG-induced ROS damage. In the following paragraph (starting at line 81), we emphasize that membrane potential can be controlled much better using channelrhodopsins, which is why we employed them in the present study.

(3) Line 78: I appreciate the concept of 'high curvature', but please always state which parameter(s) you are referring to (membrane voltage in space/time, etc?).

We corrected our statement to include the specification of space curvature of the depolarised region:

“In such a system, it was previously observed that spatiotemporal illumination can give rise to collective behaviour and ectopic waves (Teplenin et al. (2018)) originating from illuminated/depolarised regions (with high spatial curvature).”

(4) Line 79: 'bi-stable state' - not yet properly introduced in this context.

The bi-stability mentioned here refers back to single cell bistability introduced in Teplenin et al. (2018), which we cited again for clarity.

“These waves resulted from the interplay between the diffusion current and the single cell bi-stable state (Teplenin et al. (2018)) that was induced in the illuminated region.”

(5) Line 84-85: 'these ion channels allow the cells to respond' - please describe the channel used; and please correct: the channels respond to light, not the cells. Re-ordering this paragraph may help, because first you introduce channels for depolarization, then you go back to both de- and hyperpolarization. On the same note, which channels can be used for hyperpolarization of cardiomyocytes? I am not aware of any, even WiChR shows depolarizing effects in cardiomyocytes during prolonged activation (Vierock et al. 2022). Please delete: 'through a direct pathway' (Channelrhodopsins a directly light-gated channels, there are no pathways involved).

We realised that the confusion arose from our use of incorrect terminology: we mistakenly wrote hyperpolarisation instead of repolarisation. In addition to channelrhodopsins such as WiChR, other tools can also induce a repolarising effect, including light-activatable chloride pumps (e.g., JAWS). However, to improve clarity, we recognize that repolarisation is not relevant to our manuscript and therefore decided to remove its mention (see below). Regarding the reported depolarising effects of WiChR in Vierock et al. (2022), we speculate that these may arise either from the specific phenotype of the cardiomyocytes used in the study, i.e. human induced pluripotent stem cell-derived atrial myocytes (aCMs), or from the particular ionic conditions applied during patch-clamp recordings (e.g., a bath solution containing 1 mM KCl). Notably, even after prolonged WiChR activation, the aCMs maintained a strongly negative maximum diastolic potential of approximately –55 mV.

“Although effects of illuminating miniSOG with light might lead to formation of depolarised areas, it is difficult to control the process precisely since it depolarises cardiomyocytes indirectly. Therefore, in this manuscript, we used light-sensitive ion channels to obtain more refined control over cardiomyocyte depolarisation. These ion channels allow the cells to respond to specific wavelengths of light, facilitating direct depolarisation (Ördög et al. (2021, 2023)). By inducing cardiomyocyte depolarisation only in the illuminated areas, optogenetics enables precise spatiotemporal control of cardiac excitability, an attribute we exploit in this manuscript (Appendix 2 Figure 1).”

(6) Figure 1: What would be the y-axis of the 'energy-like curves' in B? What exactly did you plot here?

The graphs in Figure 1B are schematic representations intended to clarify the phenomenon for the reader. They do not depict actual data from any simulation or experiment. We clarified this misunderstanding by specifying that Figure 1B is a schematic representation of the effects at play in this paper.

“(B) Schematic representation showing how light intensity influences collective behaviour of excitable systems, transitioning between a stationary state (STA) at low illumination intensities and an oscillatory state (OSC) at high illumination intensities. Bi-stability occurs at intermediate light intensities, where transitions between states are dependent on periodic wave train properties. TR. OSC, transient oscillations.”

To expand slightly beyond the paper: our schematic representation was inspired by a common visualization in dynamical systems used to illustrate bi-stability (for an example, see Fig. 3 in Schleimer, J. H., Hesse, J., Contreras, S. A., & Schreiber, S. (2021). Firing statistics in the bistable regime of neurons with homoclinic spike generation. Physical Review E, 103(1), 012407.). In this framework, the y-axis can indeed be interpreted as an energy landscape, which is related to a probability measure through the Boltzmann distribution: . Here, p denotes the probability of occupying a particular state (STA or OSC). This probability can be estimated from the area (BCL × number of pulses) falling within each state, as shown in Fig. 4C. Since an attractor corresponds to a high-probability state, it naturally appears as a potential well in the landscape.

(7) Lines 92-93: 'this transition resulted for the interaction of an illuminated region with depolarized CM and an external wave train' - please consider rephrasing (it is not the region interacting with depolarized CM; and the external wave train could be explained more clearly).

We rephrased our unclear sentence as follows:

“This transition resulted from the interaction of depolarized cardiomyocytes in an illuminated region with an external wave train not originating from within the illuminated region.”

(8) Figure 2 and elsewhere: When mentioning 'frequency', please state frequency values and not cycle lengths. Please also reconsider your distinction between high and low frequencies; 200 ms (5 Hz) is actually the normal heart rate for neonatal rats (300 bpm).

In the revised version, we have clarified frequency values explicitly and included them alongside period values wherever frequency is mentioned, to avoid any ambiguity. We also emphasize that our use of "high" and "low" frequency is strictly a relative distinction within the context of our data, and not meant to imply a biological interpretation.

(9) Lines 129-131: Why not record optical maps? Voltage dynamics in the transition zone between depolarised and non-depolarised regions might be especially interesting to look at?

We would like to clarify that optical maps were recorded for every experiment, and all experimental traces of cardiac monolayer activity were derived from these maps. We agree with the reviewer that the voltage dynamics in the transition zone are particularly interesting. However, we selected the data representations that, in our view, best highlight the main mechanisms. When we analysed full voltage profiles, they didn’t add extra insights to this main mechanism. As the other reviewer noted, the manuscript already presents a wide range of regimes, so we decided not to introduce further complexity.

(10) Lines 156-157: Why was the model not adapted to match the biophysical properties (e.g., kinetics, ion selectivity, light sensitivity) of Cheriff?

The model was not adapted to the biophysical properties of Cheriff, because this would entail a whole new study involving extensive patch-clamping experiments, fitting, and calibration to model the correct properties of the ion channel. Beyond considerations of time efficiency, incorporating more specific modelling parameters would not change the essence of our findings. While numeric parameter ranges might shift, the core results would remain unchanged. This is a result of our experimental design where we applied constant illumination of long duration (6s or longer), thus making a difference in kinetical properties of an optogenetic tool irrelevant. In addition, we were able to observe qualitatively similar phenomena using many other depolarising optogenetic tools (e.g. ChR2, ReaChR, CatCh and more) in our in-vitro experiments. We ended up with Cheriff as our optotool-of-choice for the practical reasons of good light-sensitivity and a non-overlapping spectrum with our fluorescent dyes.

Therefore, computationally using a more general depolarising ion channel hints at the more general applicability of the observed phenomena, supporting our claim of a universal mechanism  (demonstrated experimentally with CheRiff and computationally with ChR2).

(11) Line 158: 1.7124 mW/mm^2 - While I understand that this is the specific intensity used as input in the model, I am convinced that the model is not as accurate to predict behaviour at this specific intensity (4 digits after the comma), especially given that the model has not been adapted to Cheriff (probably more light sensitive than ChR2). Can this be rephrased?

We did not aim for quantitative correspondence between the computational model and the biological experiments, but rather for qualitative agreement and mechanistic insight (see line 157). Qualitative comparisons are computationally obtained in a whole range of different intensities, as demonstrated in the 3D diagram of Fig. 4C. We wanted to demonstrate that at one fixed light intensity (chosen to be 1.7124 mW/mm^2 for the most clear effect), it was possible for all three states (STA, OSC. TR. OSC.) to coexist depending on the number of pulses and their period. Therefore the specific intensity used in the computational model is correct, and for reproducibility, we have left it unchanged while clarifying that it refers specifically to the in silico model:

“Simulating at a fixed constant illumination of 1.7124 𝑚𝑊∕𝑚𝑚² and a fixed number of 4 pulses, frequency dependency of collective bi-stability was reproduced in Figure 4A.”

(12) Lines 160, 165, and elsewhere: 'Once again, Once more' - please delete or rephrase.

We agree that we could have written these binding words better and reformulated them to:

“Similar to the experimental observations, only intermediate electrical pacing frequencies (500-𝑚𝑠 period) caused transitions from collective stationary behaviour to collective oscillatory behaviour and ectopic pacemaker activity had periods (710 𝑚𝑠) that were different from the stimulation train period (500 𝑚𝑠). Figure 4B shows the accumulation of pulses necessary to invoke a transition from the collective stationary state to the collective oscillatory state at a fixed stimulation period (600 𝑚𝑠). Also in the in silico simulations, ectopic pacemaker activity had periods (750 𝑚𝑠) that were different from the stimulation train period (600 𝑚𝑠). Also for the transient oscillatory state, the simulations show frequency selectivity (Appendix 2 Figure 4B).”

(13) Line 171: 'illumination strength': please refer to 'light intensity'.

We have revised our formulation to now refer specifically to “light intensity”:

“We previously identified three important parameters influencing such transitions: light intensity, number of pulses, and frequency of pulses.”

(14) Lines 187-188: 'the illuminated region settles into this period of sending out pulses' - please rephrase, the meaning is not clear.

We reformulated our sentence to make its content more clear to the reader:

“For the conditions that resulted in stable oscillations, the green vertical lines in the middle and right slices represent the natural pacemaker frequency in the oscillatory state. After the transition from the stationary towards the oscillatory state, oscillatory pulses emerging from the illuminated region gradually dampen and stabilize at this period, corresponding to the natural pacemaker frequency.”

(15) Figure 7: A)- please state in the legend which parameter is plotted on the y-axis (it is included in the main text, but should be provided here as well); C) The numbers provided in brackets are confusing. Why is (4) a high pulse number and (3) a low pulse number? Why not just state the number of pulses and add alpha, beta, gamma, and delta for the panels in brackets? I suggest providing the parameters (e.g., 800 ms cycle length, 2 pulses, etc) for all combinations, but not rate them with low, high, etc. (see also comment above).

We appreciate the reviewer’s comments and have revised the caption for figure 7, which now reads as follows:

“Figure 7. Phase plane projections of pulse-dependent collective state transitions. (A) Phase space trajectories (displayed in the Voltage – x_r plane) of the NRVM computational model show a limit cycle (OSC) that is not lying around a stable fixed point (STA). (B) Parameter space slice showing the relationship between stimulation period and number of pulses for a fixed illumination intensity (1.72 𝑚𝑊 ∕𝑚𝑚2) and size of the illuminated area (67 pixels edge length). Letters correspond to the graphs shown in C. (C) Phase space trajectories for different combinations of stimulus train period and number of pulses (α: 800 ms cycle length + 2 pulses, β: 800 ms cycle length + 4 pulses, γ: 250 ms cycle length + 3 pulses, δ: 250 ms cycle length + 8 pulses). α and δ do not result in a transition from the resting state to ectopic pacemaker activity, as under these circumstances the system moves towards the stationary stable fixed point from outside and inside the stable limit cycle, respectively. However, for β and γ, the stable limit cycle is approached from outside and inside, respectively, and ectopic pacemaker activity is induced.”

(16) Line 258: 'other dimensions by the electrotonic current' - not clear, please rephrase and explain.

We realized that our explanation was somewhat convoluted and have therefore changed the text as follows:

“Rather than producing oscillations, the system returns to the stationary state along dimensions other than those shown in Figure 7C (Voltage and x_r), as evidenced by the phase space trajectory crossing itself. This return is mediated by the electrotonic current.”

(17) Line 263: ‘increased too much’ – please rephrase using scientific terminology.

We rephrased our sentence to:

“However, this is not a Hopf bifurcation, because in that case the system would not return to the stationary state when the number of pulses exceeds a critical threshold.”

(18) Line 275: 'stronger diffusion/electrotonic influence from the non-illuminated region' - not sure diffusion is the correct term here. Please explain by taking into account the membrane potential. Please make sure to use proper terminology. The same applies to lines 281-282.

We appreciate this comment, which prompted us to revisit on our text. We realised that some sections could be worded more clearly, and we also identified an error in the legend of Supplementary Figure 7. The corresponding corrections are provided below:

“However, repolarisation reserve does have an influence, prolonging the transition when it is reduced (Appendix 2 Figure 7). This effect can be observed either by moving further from the boundary of the illuminated region, where the electrotonic influence from the non-illuminated region is weaker, or by introducing ionic changes, such as a reduction in I_Ks and/or I_to. For example, because the electrotonic influence is weaker in the center of the illuminated region, the voltage there is not pulled down toward the resting membrane potential as quickly as in cells at the border of the illuminated zone.”

“To add a multicellular component to our single cell model we introduced a current that replicates the effect of cell coupling and its associated electrotonic influence.”

“Figure 7. The effect of ionic changes on the termination of pacemaker activity. The mechanism that moves the oscillating illuminated tissue back to the stationary state after high frequency pacing is dependent on the ionic properties of the tissue, i.e. lower repolarisation reserves (20% 𝐼_𝐾𝑠 + 50% 𝐼_𝑡𝑜) are associated with longer transition times.”

(19) Line 289: -58 mV (to be corrected), -20 mV, and +50 mV - please justify the selection of parameters chosen. This also applies elsewhere- the selection of parameters seems quite arbitrary, please make sure the selection process is more transparent to the reader.

Our choice of parameters was guided by the dynamical properties of the illuminated cells as well as by illustrative purposes. The value of –58 mV corresponds to the stimulation threshold of the model. The values of 50 mV and –20 mV match those used for single-cell stimulation (Figure 8C2, right panel), producing excitable and bistable dynamics, respectively. We refer to this point in line 288 with the phrase “building on this result.” To maintain conciseness, we did not elaborate on the underlying reasoning within the manuscript and instead reported only the results.

We also corrected the previously missed minus sign: -58 mV.

(20) Figure 8 and corresponding text: I don't understand what stimulation with a voltage means. Is this an externally applied electric field? Or did you inject a current necessary to change the membrane voltage by this value? Please explain.

Stimulation with a specific voltage is a standard computational technique and can be likened to performing a voltage-clamp experiment on each individual cell. In this approach, the voltage of every cell in the tissue is briefly forced to a defined value.

(21) Figure 8C- panel 2: Traces at -20 mV and + 50 mV are identical. Is this correct? Please explain.

Yes, that is correct. The cell responds similarly to a voltage stimulus of -20 mV or one of 50 mV, because both values are well above the excitation threshold of a cardiomyocyte.

(22) Line 344 and elsewhere: 'diffusion current' - This is probably not the correct terminology for gap-junction mediated currents. Please rephrase.

A diffusion current is a mathematical formulation for a gap junction mediated current here, so , depending on the background of the reader, one of the terms might be used focusing on different aspects of the results. In a mathematical modelling context one often refers to a diffusion current because cardiomyocytes monolayers and tissues can be modelled using a reaction-diffusion equation. From the context of fine-grain biological and biophysical details, one uses the term gap-junction mediated current. Our choice is motivated by the main target audience we have in mind, namely interdisciplinary researchers with a core background in the mathematics/physics/computer science fields.

However, to not exclude our secondary target audience of biological and medical readers we now clarified the terminology, drawing the parallel between the different fields of study at line 79:

“These waves resulted from the interplay between the diffusion current (also known in biology/biophysics as the gap junction mediated current) and the bi-stable state that was induced in the illuminated region.”

(23) Lines 357-58: 'Such ectopic sources are typically initiated by high frequency pacing' - While this might be true during clinical testing, how would you explain this when not externally imposed? What could be biological high-frequency triggers?

Biological high-frequency triggers could include sudden increases in heart rates, such as those induced by physical activity or emotional stress. Another possibility is the occurrence of paroxysmal atrial or ventricular fibrillation, which could then give rise to an ectopic source.

(24) Lines 419-420: 'large ionic cell currents and small repolarising coupling currents'. Are coupling currents actually small in comparison to cellular currents? Can you provide relative numbers (~ratio)?

Coupling currents are indeed small compared to cellular currents. This can be inferred from the I-V curve shown in Figure 8C1, which dips below 0 and creates bi-stability only because of the small coupling current. If the coupling current were larger, the system would revert to a monostable regime. To make this more concrete, we have now provided the exact value of the coupling current used in Figure 8C1.

“Otherwise, if the hills and dips of the N-shaped steady-state IV curve were large (Figure 8C-1), they would have similar magnitudes as the large currents of fast ion channels, preventing the subtle interaction between these strong ionic cell currents and the small repolarising coupling currents (-0.103649 ≈ 0.1 pA).”

(25) Line 426: Please explain how ‘voltage shocks’ were modelled.

We would like to refer the reviewer to our response to comment (20) regarding how we model voltage shocks. In the context of line 426, a typical voltage shock corresponds to a tissue-wide stimulus of 50 mV. Independent of our computational model, line 426 also cites other publications showing that, in clinical settings, high-voltage shocks are unable to terminate ectopic sustained activity, consistent with our findings.

(26) Lines 429 ff: 0.2pA/pF would correspond to 20 pA for a small cardiomyocyte of 100 pF, this current should be measurable using patch-clamp recordings.

In trying to be succinct, we may have caused some confusion. The difference between the dips (≈-0.07 pA/pF) and hills (≈0.11 pA/pF) is approximately 0.18 pA/pF. For a small cardiomyocyte, this corresponds to deviations from zero of roughly ±10 pA. Considering that typical RMS noise levels in whole-cell patch-clamp recordings range from 2-10 pA , it is understandable that detecting these peaks and dips in an I-V curve (average current after holding a voltage for an extended period)  is difficult. Achieving statistical significance would therefore require patching a large number of cells.

Given the already extensive scope of our manuscript in terms of techniques and concepts, we decided not to pursue these additional patch-clamp experiments.

Reviewer #2 (Recommendations for the authors):

Given the deluge of conditions to consider, there are several areas of improvement possible in communicating the authors' findings. I have the following suggestions to improve the manuscript.

(1) Please change "pulse train" straight pink bar OR add stimulation marks (such as "*", or individual pulse icons) to provide better visual clarity that the applied stimuli are "short ON, long OFF" electrical pulses. I had significant initial difficulty understanding what the pulse bars represented in Figures 2, 3, 4A-B, etc. This may be partially because stimuli here could be either light (either continuous or pulsed) or electrical (likely pulsed only). To me, a solid & unbroken line intuitively denotes a continuous stimulation. I understand now that the pink bar represents the entire pulse-train duration, but I think readers would be better served with an improvement to this indicator in some fashion. For instance, the "phases" were much clearer in Figures 7C and 8D because of how colour was used on the Vm(t) traces. (How you implement this is up to you, though!)

We have addressed the reviewer’s concern and updated the figures by marking each external pulse with a small vertical line (see below).

(2) Please label the electrical stimulation location (akin to the labelled stimulation marker in circle 2 state in Figure 1A) in at least Figures 2 and 4A, and at most throughout the manuscript. It is unclear which "edge" or "pixel" the pulse-train is originating from, although I've assumed it's the left edge of the 2D tissue (both in vitro and silico). This would help readers compare the relative timing of dark blue vs. orange optical signal tracings and to understand how the activation wavefront transverses the tissue.

We indicated the pacing electrode in the optical voltage recordings with a grey asterisk. For the in silico simulations, the electrode was assumed to be far away, and the excitation was modelled as a parallel wave originating from the top boundary, indicated with a grey zone.

(3) Given the prevalence of computational experiments in this study, I suggest considering making a straightforward video demonstrating basic examples of STA, OSC, and TR.OSC states. I believe that a video visualizing these states would be visually clarifying to and greatly appreciated by readers. Appendix 2 Figure 3 would be the no-motion visualization of the examples I'm thinking of (i.e., a corresponding stitched video could be generated for this). However, this video-generation comment is a suggestion and not a request.

We have included a video showing all relevant states, which is now part of the Supplementary Material.

(4) Please fix several typos that I found in the manuscript:

(4A) Line 279: a comma is needed after i.e. when used in: "peculiar, i.e. a standard". However, this is possibly stylistic (discard suggestion if you are consistent in the manuscript).

(4B) Line 382: extra period before "(Figure 3C)".

(4C) Line 501: two periods at end of sentence "scientific purposes.." .

We would like to thank the reviewer for pointing out these typos. We have corrected them and conducted an additional check throughout the manuscript for minor errors.

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Author Response

The following is the authors’ response to the current reviews.

Public Reviews:

Reviewer #2 (Public Review):

Summary:

This paper tests the idea that schooling can provide an energetic advantage over solitary swimming. The present study measures oxygen consumption over a wide range of speeds, to determine the differences in aerobic and anaerobic cost of swimming, providing a potentially valuable addition to the literature related to the advantages of group living.

Response: Thank you for the positive comments.

Strengths:

The strength of this paper is related to providing direct measurements of the energetics (oxygen consumption) of fish while swimming in a group vs solitary. The energetic advantages of schooling has been claimed to be one of the major advantages of schooling and therefore a direct energetic assessment is a useful result.

Response: Thank you for the positive comments.

Weaknesses:

1) Regarding the fish to water volume ratio, the arguments raised by the authors are valid. However, the ratio used is still quite high (as high as >2000 in solitary fish), much higher than that recommended by Svendsen et al (2006). Hence this point needs to be discussed in the ms (summarising the points raised in the authors' response)

Response: Thank you for the comments. We have addressed this point in the previous comments. In short, our ratio is within the range of the published literature. We conducted the additional signal-to-noise analysis for quality assurance.

2) Wall effects: Fish in a school may have been swimming closer to the wall. The fact that the convex hull volume of the fish school did not change as speed increased is not a demonstration that fish were not closer to the wall, nor is it a demonstration that wall effect were not present. Therefore the issue of potential wall effects is a weakness of this paper.

Response: Thank you for the comments. We have addressed this point in the previous comments. We provided many other considerations in addition to the convex hull volume. In particular, our boundary layer is < 2.5mm, which was narrower than the width of the giant danio of ~10 mm.

3) The authors stated "Because we took high-speed videos simultaneously with the respirometry measurements, we can state unequivocally that individual fish within the school did not swim closer to the walls than solitary fish over the testing period". This is however not quantified.

Response: Thank you for the comments. We have addressed this point in the previous comments. We want to note that the statement in the response letter is to elaborate the discussion points, but not stated as data in the manuscript. The bottom line is very few studies used PIV to quantify the thickness of the boundary layer like what we did in our experiment.

4) Statistical analysis. The authors have dealt satisfactorily with most of the comments.

However :

(a) the following comment has not been dealt with directly in the ms "One can see from the graphs that schooling MO2 tends to have a smaller SD than solitary data. This may well be due to the fact that schooling data are based on 5 points (five schools) and each point is the result of the MO2 of five fish, thereby reducing the variability compared to solitary fish."

(b) Different sizes were used for solitary and schooling fishes. The authors justify using larger fish as solitary to provide a better ratio of respirometer volume to fish volume in the tests on individual fish. However, mass scaling for tail beat frequency was not provided. Although (1) this is because of lack of data for this species and (2) using scaling exponent of distant species would introduce errors of unknown magnitude, this is still a weakness of the paper that needs to be acknowledged here and in the ms.

Response: Thank you for the comments. We have addressed both points in the previous comments and provided comprehensive discussions. We also stated the caveats in the method section of the manuscript.

Reviewer #3 (Public Review):

Zhang and Lauder characterized both aerobic and anaerobic metabolic energy contributions in schools and solitary fishes in the Giant danio (Devario aequipinnatus) over a wide range of water velocities. By using a highly sophisticated respirometer system, the authors measure the aerobic metabolisms by oxygen uptake rate and the non-aerobic oxygen cost as excess post-exercise oxygen consumption (EPOC). With these data, the authors model the bioenergetic cost of schools and solitary fishes. The authors found that fish schools have a J-shaped metabolism-speed curve, with reduced total energy expenditure per tail beat compared to solitary fish. Fish in schools also recovered from exercise faster than solitary fish. Finally, the authors conclude that these energetic savings may underlie the prevalence of coordinated group locomotion in fish.

The conclusions of this paper are mostly well supported by data.

Response: Thank you for the positive comments.

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

I have read carefully the revised version of the manuscript and would like to thank the authors for addressing all my comments/suggestions.

I have no additional comments/suggestions. Now, I strongly believe that this manuscript deserves to be published in eLife.

Response: Thank you for the positive comments.

The following is the authors’ response to the original reviews.

General responses

Many thanks to the reviewers and editors for their very helpful comments on our manuscript. Below we respond (in blue text) to each of the reviewer comments, both the public ones and the more detailed individual comments in the second part of each review. In some cases, we consider these together where the same point is made in both sets of comments. We have made several changes to the manuscript in response to reviewer suggestions, and we respond in detail to the comments of reviewer #2 who feels that we have overstated the significance of our manuscript and suggests several relevant literature references. We prepared a table summarizing these references and why they differ substantially from the approach taken in our paper here.

Overall, we would like to emphasize to both reviewers and readers of this response document that previous studies of fish schooling dynamics (or collective movement of vertebrates in general, see Commentary Zhang & Lauder 2023 J. Exp. Biol., doi:10.1242/jeb.245617) have not considered a wide speed range and thus the importance of measuring EPOC (excess post-exercise oxygen consumption) as a key component of energy use. Quantifying both aerobic and non-aerobic energy use allows us to calculate the total energy expenditure (TEE) which we show differs substantially and, importantly, non-linearly with speed between schools and measurements on solitary individuals. Comparison between school total energy use and individual total energy use are critical to understanding the dynamics of schooling behaviour in fishes.

The scope of this study is the energetics of fish schools. By quantifying the TEE over a wide range of swimming speeds, we also show that the energetic performance curve is concave upward, and not linear, and how schooling behaviour modifies this non-linear relationship.

In addition, one key implication of our results is that kinematic measurements of fish in schools (such as tail beat frequency) are not a reliable metric by which to estimate energy use. Since we recorded high-speed video simultaneously with energetic measurements, we are able to show that substantial energy savings occur by fish in schools with little to no change in tail beat frequency, and we discuss in the manuscript the various fluid dynamic mechanisms that allow this. Indeed, studies of bird flight show that when flying in a (presumed) energy-saving V-formation, wing beat frequency can actually increase compared to flying alone. We believe that this is a particularly important part of our findings: understanding energy use by fish schools must involve actual measurements of energy use and not indirect and sometimes unreliable kinematic measurements such as tail beat frequency or amplitude.

Reviewer #1 (Public Review):

Summary:

In the presented manuscript the authors aim at quantifying the costs of locomotion in schooling versus solitary fish across a considerable range of speeds. Specifically, they quantify the possible reduction in the cost of locomotion in fish due to schooling behavior. The main novelty appears to be the direct measurement of absolute swimming costs and total energy expenditure, including the anaerobic costs at higher swimming speeds.

In addition to metabolic parameters, the authors also recorded some basic kinematic parameters such as average distances or school elongation. They find both for solitary and schooling fish, similar optimal swimming speeds of around 1BL/s, and a significant reduction in costs of locomotion due to schooling at high speeds, in particular at ~5-8 BL/s.

Given the lack of experimental data and the direct measurements across a wide range of speeds comparing solitary and schooling fish, this appears indeed like a potentially important contribution of interest to a broader audience beyond the specific field of fish physiology, in particular for researchers working broadly on collective (fish) behavior.

Response: Thank you for seeing the potential implications of this study. We also believe that this paper has broader implications for collective behaviour in general, and outline some of our thinking on this topic in a recent Commentary article in the Journal of Experimental Biology: (Zhang & Lauder 2023 doi:10.1242/jeb.245617). Understanding the energetics of collective behaviours in the water, land, and air is a topic that has not received much attention despite the widespread view that moving as a collective saves energy.

Strengths:

The manuscript is for the most part well written, and the figures are of good quality. The experimental method and protocols are very thorough and of high quality. The results are quite compelling and interesting. What is particularly interesting, in light of previous literature on the topic, is that the authors conclude that based on their results, specific fixed relative positions or kinematic features (tail beat phase locking) do not seem to be required for energetic savings. They also provide a review of potential different mechanisms that could play a role in the energetic savings.

Response: Thank you for seeing the nuances we bring to the existing literature and comment on the quality of the experimental method and protocols. Despite a relatively large literature on fish schooling based on previous biomechanical research, our studies suggest that direct measurement of energetic cost clearly demonstrates the energy savings that result from the sum of different fluid dynamic mechanisms depending on where fish are, and also emphasizes that simple metrics like fish tail beat frequency do not adequately reflect energy savings during collective motion.

Weaknesses:

A weakness is the actual lack of critical discussion of the different mechanisms as well as the discussion on the conjecture that relative positions and kinematic features do not matter. I found the overall discussion on this rather unsatisfactory, lacking some critical reflections as well as different relevant statements or explanations being scattered across the discussion section. Here I would suggest a revision of the discussion section.

Response: The critical discussion of the different possible energy-saving mechanisms is indeed an important topic. We provided a discussion about the overall mechanism of ‘local interactions’ in the first paragraph of “Schooling Dynamics and energy conservation”. To clarify, our aim with Figure 1 is to introduce the current mechanisms proposed in the existing engineering/hydrodynamic literature that have studied a number of possible configurations both experimentally and computationally. Thank you for the suggestion of better organizing the discussion to critically highlight different mechanisms that would enable a dynamic schooling structure to still save energy and why the appendage movement frequency does not necessarily couple with the metabolic energy expenditure. Much of this literature uses computational fluid dynamic models or experiments on flapping foils as representative of fish. This exact issue is of great interest to us, and we are currently engaged in a number of other experiments that we hope will shed light on how fish moving in specific formations do or don’t save energy.

Our aim in presenting Figure 1 at the start of the paper was to show that there are several ways that fish could save energy when moving in a group as shown by engineering analyses, but before investigating these various mechanisms in detail we first have to show that fish moving in groups actually do save energy with direct metabolic measurements. Hence, our paper treats the various mechanisms as inspiration to determine experimentally if, in fact, fish in schools save energy, and if so how much over a wide speed range. Our focus is to experimentally determine the performance curve that shows energy use as speed increases, for schools compared to individuals. Therefore, we have elected not to go into detail about these different hydrodynamic mechanisms in this paper, but rather to present them as a summary of current engineering literature views and then proceed to document energy savings (as stated in the second last paragraph of Introduction). We have an Commentary paper in the Journal of Experimental Biology that addresses this issue generally, and we are reluctant to duplicate much of that discussion here (Zhang & Lauder 2023 doi:10.1242/jeb.245617). We are working hard on this general issue as we agree that it is very interesting. We have revised the Introduction (second last paragraph of Introduction) and Discussion (first paragraph of Discussion) to better indicate our approach, but we have not added any significant discussion of the different hydrodynamic energy saving proposals as we believe that it outside the scope of this first paper and more suitable as part of follow-up studies.

Also, there is a statement that Danio regularly move within the school and do not maintain inter-individual positions. However, there is no quantitative data shown supporting this statement, quantifying the time scales of neighbor switches. This should be addressed as core conclusions appear to rest on this statement and the authors have 3d tracks of the fish.

Response: Thank you for pointing out this very important future research direction. Based on our observations and the hypothesized mechanisms for fish within the school to save energy (Fig. 1), we have been conducting follow-up experiments to decipher the multiple dynamic mechanisms that enable the fish within the school to save energy. Tracking the 3D position of each individual fish body in 3D within the fish school has proven difficult. We currently have 3D data on the nose position obtained simultaneously with the energetic measurements, but we do not have full 3D fish body positional data. Working with our collaborators, we are developing a 3-D tracking algorithm that will allow us to quantify how long fish spend in specific formations, and we currently have a new capability to record high-speed video of fish schooling moving in a flow tank for many hours (see our recent perspective by Ko et al., 2023 doi.org/10.1098/rsif.2023.0357). The new algorithms and the results will be published as separate studies and we think that these ongoing experiments are outside the scope of the current study with its focus on energetics. Nevertheless, the main point of Fig. 1 is to provide possible mechanisms to inspire future studies to dissect the detailed hydrodynamic mechanisms for energy saving, and the points raised by this comment are indeed extremely interesting to us and our ongoing experiments in this area. We provide a statement to clarify this point in the 1st paragraph of “Schooling dynamics and energy conservation” section.

Further, there is a fundamental question on the comparison of schooling in a flow (like a stream or here flow channel) versus schooling in still water. While it is clear that from a pure physics point of view that the situation for individual fish is equivalent. As it is about maintaining a certain relative velocity to the fluid, I do think that it makes a huge qualitative difference from a biological point of view in the context of collective swimming. In a flow, individual fish have to align with the external flow to ensure that they remain stationary and do not fall back, which then leads to highly polarized schools. However, this high polarization is induced also for completely non-interacting fish. At high speeds, also the capability of individuals to control their relative position in the school is likely very restricted, simply by being forced to put most of their afford into maintaining a stationary position in the flow. This appears to me fundamentally different from schooling in still water, where the alignment (high polarization) has to come purely from social interactions. Here, relative positioning with respect to others is much more controlled by the movement decisions of individuals. Thus, I see clearly how this work is relevant for natural behavior in flows and that it provides some insights on the fundamental physiology, but I at least have some doubts about how far it extends actually to “voluntary” highly ordered schooling under still water conditions. Here, I would wish at least some more critical reflection and or explanation.

Response: We agree completely with this comment that animal group orientations in still fluid can have different causes from their locomotion in a moving fluid. We very much agree with the reviewer that social interactions in still water, which typically involve low-speed locomotion and other behaviours such as searching for food by the group, can be important and could dictate fish movement patterns. In undertaking this project, we wanted to challenge fish to move at speed, and reasoned that if energy savings are important in schooling behaviour due to hydrodynamic mechanisms, we should see this when fish are moving forward against drag forces induced by fluid impacting the school. Drag forces scale as velocity squared, so we should see energy savings by the school, if any, as speed increases.

We also quantified fish school swimming speeds in the field from the literature and presented a figure showing that in nature fish schools can and do move at considerable speeds. This figure is part of our overview on collective behaviour recently in J. Exp. Biol. (Zhang & Lauder 2023 doi:10.1242/jeb.245617). It is only by studying fish schools moving over a speed range that we can understand the performance curve relating energy use to swimming speed. Indeed, we wonder if fish moving in still water as a collective versus as solitary individuals would show energy savings at all. We now provided the justification for studying fish schooling in moving fluids in the second and third paragraph of the Introduction. When animals are challenged hydrodynamically (e.g. at higher speed), it introduces the need to save energy. Movement in still water lacks the need for fish to save energy. When fish do not need to save locomotor energy in still water, it is hard to justify why we would expect to observe energy saving and related physiological mechanisms in the first place. As the reviewer said, the ‘high polarization in still water has to come purely from social interactions’. Our study does not dispute this consideration, and indeed we agree with it! In our supplementary materials, we acknowledged the definitions for different scenarios of fish schooling can have different behavioural and ecological drivers. Using these definitions, we explicitly stated, in the introduction, that our study focuses on active and directional schooling behaviour to understand the possible hydrodynamic benefits of energy expenditure for collective movements of fish schools. By stating the scope of our study at the outset, we hope that this will keep the discussion focused on the energetics and kinematics of fish schools, without unnecessarily addressing other many possible reasons for fish schooling behaviours in the discussion such as anti-predator grouping, food searching, or reproduction as three examples.

As this being said, we acknowledge (in the 2nd paragraph of the introduction) that fish schooling behaviour can have other drivers when the flow is not challenging. Also, there are robotic-&-animal interaction studies and computational fluid dynamic simulation studies (that we cited) that show individuals in fish schools interact hydrodynamically. Hydrodynamic interactions are not the same as behaviour interactions, but it does not mean individuals within the fish schooling in moving flow are not interacting and coordinating.

Related to this, the reported increase in the elongation of the school at a higher speed could have also different explanations. The authors speculate briefly it could be related to the optimal structure of the school, but it could be simply inter-individual performance differences, with slower individuals simply falling back with respect to faster ones. Did the authors test for certain fish being predominantly at the front or back? Did they test for individual swimming performance before testing them in groups together? Again this should be at least critically reflected somewhere.

Response: Thank you for raising this point. If the more streamlined schooling structure above 2 BL/s is due to the weaker individuals not catching up with the rest of the school, we would expect the weaker individuals to quit swimming tests well before 8 BL/s. However, we did not observe this phenomenon. Although we did not specifically test for the two questions the reviewer raises here, our results suggest that inter-individual variation in the swimming performance of giant Danio is not at the range of 2 to 8 BL/s (a 400% difference). While inter-individual differences certainly exist, we believe that they are small relative to the speeds tested as we did not see any particular individuals consistently unable to keep up with the school or certain individuals maintaining a position near the back of the school. As this being said, we provide additional interpretations for the elongated schooling structure at the end of the 2nd paragraph of the “schooling dynamics and energy conservation” section.

Reviewer #1 (Recommendations For The Authors):

Line 58: The authors write "How the fluid dynamics (...) enable energetic savings (...)". However, the paper focuses rather on the question of whether energetic savings exist and does not enlighten us on the dominant mechanisms. Although it gives a brief overview of all possible mechanisms, it remains speculative on the actual fluid dynamical and biomechanical processes. Thus, I suggest changing "How" to "Whether".

Response: Great point! We changed “How” to “Whether”.

Lines 129-140: In the discussion of the U-shaped aerobic rate, there is no direct comparison of the minimum cost values between the schooling and solitary conditions. Only the minimum costs during schooling are named/discussed. In addition to the data in the figure, I suggest explicitly comparing them as well for full transparency.

Response: Thanks for raising this point. We did not belabor this point because there was no statistical significance. As requested, we added a statement to address this with statistics in the 1st paragraph of the Results section.

Line 149: The authors note that the schooling fish have a higher turning frequency than solitary fish. Here, a brief discussion of potential explanations would be good, e.g. need for coordination with neighbors -> cost of schooling.

Response: Thank you for the suggestion. In the original version of the manuscript, we discussed that the higher turning frequency could be related to higher postural costs for active stability adjustment at low speeds. As requested, we now added that high turn frequency can relate to the need for coordination with neighbours in the last paragraph of the “Aerobic metabolic rate–speed curve of fish schools” section. As indicated above, the suspected costs of coordination did not result in higher costs of schooling at the lower speed (< 2 BL s-1, where the turn frequency is higher).

Line 151: The authors discuss the higher maximum metabolic rate of schooling fish as a higher aerobic performance and lower use of aerobic capacity. This may be confusing for non-experts in animal physiology and energetics of locomotion. I recommend providing somewhere in a paper an additional explanation to clarify it to non-experts. While lines 234-240 and further below potentially address this, I found this not very focused or accessible to non-experts. Here, I suggest the authors consider revisions to make it more comprehensible to a wider, interdisciplinary audience.

Response: We agree with the reviewer that the difference between maximum oxygen uptake and maximum metabolic rate can be confusing. In fact, among animal physiologists, these two concepts are often muddled. One of the authors is working on an invited commentary from J. Exp. Biol. to clearly define these two concepts. We have made the language in the section “Schooling dynamics enhances aerobic performance and reduces non-aerobic energy use” more accessible to a general audience. In addition, the original version presented the relevant framework in the first and the second paragraphs of the Introduction when discussing aerobic and non-aerobic energy contribution. In brief, when vertebrates exhibit maximum oxygen uptake, they use aerobic and non-aerobic energy contributions that both contribute to their metabolic rate. Therefore, the maximum total metabolic rate is higher than the one estimated from only maximum oxygen uptake. We used the method presented in Fig. 3a to estimate the maximum metabolic rate for metabolic energy use (combining aerobic and non-aerobic energy use). In kinesiology, maximum oxygen uptake is used to evaluate the aerobic performance and energy use of human athletes is estimated by power meters or doubly labelled water.

Line 211: The authors write that Danio regularly move within the school and do not maintain inter-individual positions. Given that this is an important observation, and the relative position and its changes are crucial to understanding the possible mechanisms for energetic savings in schools, I would expect some more quantitative support for this statement, in particular as the authors have access to 3d tracking data. For example introducing some simple metrics like average time intervals between swaps of nearest neighbors, possibly also resolved in directions (front+back versus right+left), should provide at least some rough quantification of the involved timescales, whether it is seconds, tens of seconds, or minutes.

Response: As responded in the comment above, 3-D tracking of both body position and body deformation of multiple individuals in a school is not a trivial research challenge and we have ongoing research on this issue. We hope to have results on the 3D positions of fish in schools soon! For this manuscript, we believe that the data in Figure 4E which shows the turning frequency of fish in schools and solitary controls shows the general phenomenon of fish moving around (as fish turn to change positions within the school), but we agree that more could be done to address this point and we are indeed working on it now.

Lines 212-217: There is a very strong statement that energetic savings by collective motion do not require fixed positional arrangements or specific kinematic features. While possibly one of the most interesting findings of the paper, I found that in its current state, it was not sufficiently/satisfactorily discussed. For example for the different mechanisms summarized, there will be clearly differences in their relevance based on relative distance and position. For example mechanisms 3 and 4 likely have significant contributions only at short distances. Here, the question is how relevant can they be if the average distance is 1 BL? Also, 1BL side by side is very much different from 1BL front to back, given the elongated body shape. For mechanisms 1 and 2, it appears relative positioning is quite important. Here, having maybe at least some information from the literature (if available) on the range of wall or push effects or the required precision in relative positioning for having a significant benefit would be very much desired. Also, do the authors suggest that a) these different effects overlap giving any position in the school a benefit, or b) that there are specific positions giving benefits due to different mechanisms and that fish "on purpose" switch only between these energetic "sweet" spots, I guess this what is towards the end referred to as Lighthill conjecture? Given the small group size I find a) rather unlikely, while b) actually also leads to a coordination problem if every fish is looking for a sweet spot. Overall, a related question is whether the authors observed a systematic change in leading individuals, which likely have no, or very small, hydrodynamic benefits.

Response: Thank you for the excellent discussion on this point. As we responded above, we have softened the tone of the statement. In the original version, we were clear that the known mechanisms as summarized in Fig. 1 lead us to ‘expect’ that fish do not need to be in a fixed position to save energy.

In general, current engineering/hydrodynamic studies suggest that any fish positioned within one body length (both upstream and downstream and side by side) will benefit from one or more of the hydrodynamic mechanisms that we expect will reduce energy costs, relative to a solitary individual. Our own studies using robotic systems suggest that a leading fish will experience an added mass “push” from a follower when the follower is located within roughly ½ body length behind the leader. We cited a Computational Fluid Dynamic (CFD) study about the relative distance among individuals for energy saving to be in effect. Please keep in mind that CFD simulation is a simplified model of the actual locomotion of fish and involves many assumptions and currently only resolves the time scale of seconds (see commentary of Zhang & Lauder 2023 doi:10.1242/jeb.245617 in J. Exp. Biol. for the current challenges of CFD simulation). To really understand the dynamic positions of fish within the school, we will need 3-D tracking of fish schools with tools that are currently being developed. Ideally, we would also have simultaneous energetic measurements, but of course, this is enormously challenging and it is not clear at this time how to accomplish this.

We certainly agree that the relative positions of fish (vertically staggered or in-line swimming) do affect the specific hydrodynamic mechanisms being used. We cited the study that discussed this, but the relative positions of fish remain an active area of research. More studies will be out next few years to provide more insight into the effects of the relative positions of fish in energy saving. The Lighthill conjecture is observed in flapping foils and whether fish schools use the Lighthill conjecture for energy saving is an active area of research but still unclear. We also provided a citation about the implication of the Lighthill conjecture on fish schools. Hence, our original version stated ‘The exact energetic mechanisms….would benefit from more in-depth studies’. We agree with the reviewer that not all fish can benefit Lighthill conjecture (if fish schools use it) at any given time point, hence the fish might need to rotate in using the Lighthill conjecture. This is one more explanation for the dynamic positioning of fish in a school.

Overall, in response to the question raised, we do not believe that fish are actively searching for “sweet spots” within the school, although this is only speculation on our part. We believe instead that fish, located in a diversity of positions within the school, get the hydrodynamic advantage of being in the group at that configuration.

We believe that fish, once they group and maintain a grouping where individuals are all within around one body length distance from each other, will necessarily get hydrodynamic benefits. As a collective group, we believe that at any one time, several different hydrodynamic mechanisms are all acting simultaneously and result in reduced energetic costs (Fig. 1).

Figure 4E: The y-axis is given in the units of 10-sec^-1 which is confusing is it 10 1/s or 1/(10s)? Why not use simply the unit of 1/s which is unambiguous?

Response: Thank you for the suggestions. We counted the turning frequency over the course of 10 seconds. To reflect more accurately on what we did, we used the suggested unit of 1/(10s) to more correctly correspond to how we made the measurements and the duration of the measurement. We recognize that this is a bit non-standard but would like to keep these units if possible.

Figure 4F: The unit in the school length is given in [mm], which suggests that the maximal measured school length is 4mm, this can't be true.

Response: Thank you for pointing this out. The unit should be [cm], which we corrected.

Reviewer #2 (Public Review):

Summary:

This paper tests the idea that schooling can provide an energetic advantage over solitary swimming. The present study measures oxygen consumption over a wide range of speeds, to determine the differences in aerobic and anaerobic cost of swimming, providing a potentially valuable addition to the literature related to the advantages of group living.

Response: Thank you for acknowledging our contribution is a valuable addition to the literature on collective movement by animals.

Strengths:

The strength of this paper is related to providing direct measurements of the energetics (oxygen consumption) of fish while swimming in a group vs solitary. The energetic advantages of schooling have been claimed to be one of the major advantages of schooling and therefore a direct energetic assessment is a useful result.

Response: Thank you for acknowledging our results are useful and provide direct measurements of energetics to prove a major advantage of schooling relative to solitary motion over a range of speeds.

Weaknesses:

The manuscript suffers from a number of weaknesses which are summarised below:

1) The possibility that fish in a school show lower oxygen consumption may also be due to a calming effect. While the authors show that there is no difference at low speed, one cannot rule out that calming effects play a more important role at higher speed, i.e. in a more stressful situation.

Response: Thank you for raising this creative point on “calming”. When vertebrates are moving at high speeds, their stress hormones (adrenaline, catecholamines & cortisol) increase. This phenomenon has been widely studied, and therefore, we do not believe that animals are ‘calm’ when moving at high speed and that somehow a “calming effect” explains our non-linear concave-upward energetic curves. “Calming” would have to have a rather strange non-linear effect over speed to explain our data, and act in contrast to known physiological responses involved in intense exercise (whether in fish or humans). It is certainly not true for humans that running at high speeds in a group causes a “calming effect” that explains changes in metabolic energy expenditure. We have added an explanation in the third paragraph in the section “Schooling dynamics enhances aerobic performance and reduces non-aerobic energy use”. Moreover, when animal locomotion has a high frequency of appendage movement (for both solitary individual and group movement), they are also not ‘calm’ from a behavioural point of view. Therefore, we respectfully disagree with the reviewer that the ‘calming effect’ is a major contributor to the energy saving of group movement at high speed. It is difficult to believe that giant danio swimming at 8 BL/s which is near or at their maximal sustainable locomotor limits are somehow “calm”. In addition, we demonstrated by direct energetic measurement that solitary individuals do not have a higher metabolic rate at the lower speed and thus directly show that there is very likely no cost of “uncalm” stress that would elevate the metabolic rate of solitary individuals. Furthermore, the current version of this manuscript compared the condition factor of the fish in the school and solitary individuals and found no difference (see Experimental Animal Section in the Methods). This also suggests that the measurement on the solitary fish is likely not confounded by any stress effects.

Finally, and as discussed further below, since we have simultaneous high-speed videos of fish swimming as we measure oxygen consumption at all speeds, we are able to directly measure fish behaviour. Since we observed no alteration in tail beat kinematics between schools and individuals (a key result that we elaborate on below), it’s very hard to justify that a “calming” effect explains our results. Fish in schools swimming at speed (not in still water) appear to be just as “calm” as solitary individuals.

2) The ratio of fish volume to water volume in the respirometer is much higher than that recommended by the methodological paper by Svendsen et al. (J Fish Biol 2016) Response: The ratio of respirometer volume to fish volume is an important issue that we thought about in detail before conducting these experiments. While Svendsen et al., (J. Fish Biol. 2016) recommend a respirometer volume-to-fish volume ratio of 500, we are not aware of any experimental study comparing volumes with oxygen measuring accuracy that gives this number as optimal. In addition, the Svendsen et al. paper does not consider that their recommendation might result in fish swimming near the walls of the flume (as a result of having relatively larger fish volume to flume volume) and hence able to alter their energetic expenditure by being near the wall. In our case, we needed to be able to study both a school (with higher animal volumes) and an individual (relatively lower volume) in the same exact experimental apparatus. Thus, we had to develop a system to accurately record oxygen consumption under both conditions.

The ratio of our respirometer to individual volume for schools is 693, while the value for individual fish is 2200. Previous studies (Parker 1973, Abrahams & Colgan, 1985, Burgerhout et al., 2013) that used a swimming-tunnel respirometer (i.e., a sealed treadmill) to measure the energy cost of group locomotion used values that range between 1116 and 8894 which are large and could produce low-resolution measurements of oxygen consumption. Thus, we believe that we have an excellent ratio for our experiments on both schools and solitary individuals, while maintaining a large enough value that fish don’t experience wall effects (see more discussion on this below, as we experimentally quantified the flow pattern within our respirometer).

The goal of the recommendation by Svendsen et al. is to achieve a satisfactory R2 (coefficient of determination) value for oxygen consumption data. However, Chabot et al., 2020 (DOI: 10.1111/jfb.14650) pointed out that only relying on R2 values is not always successful at excluding non-linear slopes. Much worse, only pursuing high R2 values has a risk of removing linear slopes with low R2 only because of a low signal-to-noise ratio and resulting in an overestimation of the low metabolic rate. Although we acknowledge the excellent efforts and recommendations provided by Svendsen et al., 2016, we perhaps should not treat the ratio of respirometer to organism volume of 500 as the gold standard for swim-tunnel respirometry. Svendsen et al., 2020 did not indicate how they reached the recommendation of using the ratio of respirometer to organism volume of 500. Moreover, Svendsen et al., 2020 stated that using an extended measuring period can help to resolve the low signal-to-noise ratio. Hence, the key consideration is to obtain a reliable signal-to-noise ratio which we will discuss below.

To ensure we obtain reliable data quality, we installed a water mixing loop (Steffensen et al., 1984) and used the currently best available technology of oxygen probe (see method section of Integrated Biomechanics & Bioenergetic Assessment System) to improve the signal-to-noise ratio. The water mixing loop is not commonly used in swim-tunnel respirometer. Hence, if a previously published study used a respirometer-to-organism ratio up to 8894, our updated oxygen measuring system is completely adequate to produce reliable signal-to-noise ratios in our system with a respirometer-to-organism ratio of 2200 (individuals) and 693 (schools). In fact, our original version of the manuscript used a published method (Zhang et al., 2019, J. Exp. Biol. https://doi.org/10.1242/jeb.196568) to analyze the signal-to-noise ratio and provided the quantitative approach to determine the sampling window to reliably capture the signal (Fig. S5).

3) Because the same swimming tunnel was used for schools and solitary fish, schooling fish may end up swimming closer to the wall (because of less volume per fish) than solitary fish. Distances to the wall of schooling fish are not given, and they could provide an advantage to schooling fish.

Response: This is an issue that we considered carefully in designing these experiments. After considering the volume of the respirometer and the size of the fish (see the response above), we decided to use the same respirometer to avoid any other confounding factors when using different sizes of respirometers with potentially different internal flow patterns. In particular, different sizes of Brett-type swim-tunnel respirometers differ in the turning radius of water flow, which can produce different flow patterns in the swimming section. Please note that we quantified the flow pattern within the flow tank using particle image velocimetry (PIV) (so we have quantitative velocity profiles across the working section at all tested speeds), and modified the provided baffle system to improve the flow in the working section.

Because we took high-speed videos simultaneously with the respirometry measurements, we can state unequivocally that individual fish within the school did not swim closer to the walls than solitary fish over the testing period (see below for the quantitative measurements of the boundary layer). Indeed, many previous respirometry studies do not obtain simultaneous video data and hence are unable to document fish locations when energetics is measured.

In studying schooling energetics, we believe that it is important to control as many factors as possible when making comparisons between school energetics and solitary locomotion. We took great care as indicated in the Methods section to keep all experimental parameters the same (same light conditions, same flow tank, same O2 measuring locations with the internal flow loop, etc.) so that we could detect differences if present. Changing the flow tank respirometer apparatus between individual fish and the schools studied would have introduced an unacceptable alteration of experimental conditions and would be a clear violation of the best experimental practices.

We have made every effort to be clear and transparent about the choice of experimental apparatus and explained at great length the experimental parameters and setup used, including the considerations about the wall effect in the extended Methods section and supplemental material provided.

Our manuscript provides the measurement of the boundary layer (<2.5 mm at speeds > 2 BL s-1) in the methods section of the Integrated Biomechanics & Bioenergetic Assessment System. We also state that the boundary layer is much thinner than the body width of the giant danio (~10 mm) so that the fish cannot effectively hide near the wall. Due to our PIV calibration, we are able to quantify flow near the wall.

In the manuscript, we also provide details about the wall effects and fish schools as follows from the manuscript: ”…the convex hull volume of the fish school did not change as speed increased, suggesting that the fish school was not flattening against the wall of the swim tunnel, a typical feature when fish schools are benefiting from wall effects. In nature, fish in the centre of the school effectively swim against a ‘wall’ of surrounding fish where they can benefit from hydrodynamic interactions with neighbours.”’ The notion that the lateral motion of surrounding slender bodies can be represented by a streamlined wall was also proposed by Newman et al., 1970 J. Fluid Mech. These considerations provide ample justification for the comparison of locomotor energetics by schools and solitary individuals.

4) The statistical analysis has a number of problems. The values of MO2 of each school are the result of the oxygen consumption of each fish, and therefore the test is comparing 5 individuals (i.e. an individual is the statistical unit) vs 5 schools (a school made out of 8 fish is the statistical unit). Therefore the test is comparing two different statistical units. One can see from the graphs that schooling MO2 tends to have a smaller SD than solitary data. This may well be due to the fact that schooling data are based on 5 points (five schools) and each point is the result of the MO2 of five fish, thereby reducing the variability compared to solitary fish. Other issues are related to data (for example Tail beat frequency) not being independent in schooling fish.

Response: We cannot agree with the reviewer that fish schools and solitary individuals are different statistical units. Indeed, these are the two treatments in the statistical sense: a school versus the individual. This is why we invested extra effort to replicate all our experiments on multiple schools of different individuals and compare the data to multiple different solitary individuals. This is a standard statistical approach, whether one is comparing a tissue with multiple cells to an individual cell, or multiple locations to one specific location in an ecological study. Our analysis treats the collective movement of the fish school as a functional unit, just like the solitary individual is a functional unit. At the most fundamental level of oxygen uptake measurements, our analysis results from calculating the declining dissolved oxygen as a function of time (i.e. the slope of oxygen removal). Comparisons are made between the slope of oxygen removal by fish schools and the slope of oxygen removal by solitary individuals. This is the correct statistical comparison.

The larger SD in individuals can be due to multiple biological reasons other than the technical reasons suggested here. Fundamentally, the different SD between fish schools and individuals can be the result of differences between solitary and collective movement and the different fluid dynamic interactions within the school could certainly cause differences in the amount of variation seen. Our interpretation of the ‘numerically’ smaller SD in fish schools than that of solitary individuals suggests that interesting hydrodynamic phenomena within fish schools remain to be discovered.

Reviewer #2 (Recommendations For The Authors):

I have reviewed a previous version of this paper. This new draft is somewhat improved but still presents a number of issues which I have outlined below.

Response: Thanks for your efforts to improve our paper with reviews, but a number of your comments apply to the previous version of the paper, and we have made a number of revisions before submitting it to eLife. We explain below how this version of the manuscript addresses many of your comments from both the previous and current reviews. As readers can see from our responses below, this version of the manuscript version no longer uses only ‘two-way ANOVA’ as we have implemented an additional statistical model. (Please see the comments below for more detailed responses related to the statistical models).

1) One of the main problems, and one of the reasons (see below) why many previous papers have measured TBF and not the oxygen consumption of a whole school, is that schooling also provides a calming effect (Nadler et al 2018) which is not easily differentiated from the hydrodynamic advantages (Abraham and Colgan 1985). This effect can reduce the MO2 while swimming and the EPOC when recovering. The present study does not fully take this potential issue into account and therefore its results are confounded by such effects. The authors state (line 401) that " the aerobic locomotion cost of solitary individuals showed no statistical difference from (in fact, being numerically lower) that of fish schools at a very low testing speed. The flow speed is similar to some areas of the aerated home aquarium for each individual fish. This suggests that the stress of solitary fish likely does not meaningfully contribute to the higher locomotor costs". While this is useful, the possibility that at higher speeds (i.e. a more stressful situation) solitary fish may experience more stress than fish in a school, cannot be ruled out.

Response: Thank you for finding our results and data useful. We have addressed the comments on calming or stress effects in our response above. The key point is that either solitary or school fish are challenged (i.e. stressed) at a high speed where the sizable increases in stress hormones are well documented in the exercise physiology literature. We honestly just do not understand how a “calming” effect could possibly explain the upward concave energetic curves that we obtained, and how “calming” could explain the difference between schools and solitary individuals. Since we have simultaneous high-speed videos of fish swimming as we measure oxygen consumption at all speeds, we are able to directly observe fish behaviour. It is not exactly clear what a “calming effect” would look like kinematically or how one would measure this experimentally, but since we observed no alteration in tail beat kinematics between schools and individuals (a key result that we elaborate on below), it’s very hard to justify that a “calming” effect explains our results. Fish in schools appear to be just as “calm” as solitary individuals.

If the reviewer's “calming effect” is a general issue, then birds flying in a V-formation should also experience a “calming effect”, but at least one study shows that birds in a V-formation experience higher wing beat frequencies.

In addition, Nalder et al., 2018 (https://doi.org/10.1242/bio.031997) did not study any such “calming effect”. We assume the reviewer is referring to Nalder et al., 2016, which showed that shoaling reduced fish metabolic rates in a resting respirometer that has little-to-no water current that would motivate fish to swim (which is very different from the swim-tunnel respirometer we used). Moreover, the inter-loop system used by Nalder et al., 2016 has the risk of mixing the oxygen uptake of the fish shoal and solitary individuals. Hence, we believe that it is not appropriate to extend the results of Nalder et al., 2016 to infer and insist on a calming effect for fish schools that we studied which are actively and directionally swimming over a wide speed range up to and including high speeds. Especially since our data clearly show that ‘the aerobic locomotion cost of solitary individuals showed no statistical difference from (in fact, being numerically lower) that of fish schools at very low testing speeds’. More broadly, shoaling and schooling are very different in terms of polarization as well as the physiological and behavioural mechanisms used in locomotion. Shoaling behaviour by fish in still water is not the same as active directional schooling over a speed range. Our supplementary Table 1 provides a clear definition for a variety of grouping behaviours and makes the distinction between shoaling and schooling.

Our detailed discussion about other literature mentioned by this reviewer can be seen in the comments below.

2) The authors overstate the novelty of their work. Line 29: "Direct energetic measurements demonstrating the 30 energy-saving benefits of fluid-mediated group movements remain elusive" The idea that schooling may provide a reduction in the energetic costs of swimming dates back to the 70s, with pioneering experimental work showing a reduction in tail beat frequency in schooling fish vs solitary (by Zuyev, G. V. & Belyayev, V. V. (1970) and theoretical work by Weihs (1973). Work carried out in the past 20 years (Herskin and Steffensen 1998; Marras et al 2015; Bergerhout et al 2013; Hemelrijk et al 2014; Li et al 2021, Wiwchar et al 2017; Verma et al 2018; Ashraf et al 2019) based on a variety of approaches has supported the idea of a reduction in swimming costs in schooling vs solitary fish. In addition, group respirometry has actually been done in early and more recent studies testing the reduction in oxygen consumption as a result of schooling (Parker, 1973; Itazawa et al., 1978; Abrahams and Colgan 1985; Davis & Olla, 1992; Ross & Backman, 1992, Bergerhout et al 2013; Currier et al 2020). Specifically, Abrahams and Colgan (1985) and Bergerhout et al (2013) found that the oxygen consumption of fish swimming in a school was higher than when solitary, and Abrahams and Colgan (1985) made an attempt to deal with the confounding calming effect by pairing solitary fish up with a neighbor visible behind a barrier. These issues and how they were dealt with in the past (and in the present manuscript) are not addressed by the present manuscript. Currier et al (2020) found that the reduction of oxygen consumption was species-specific.

Response: We cannot agree with this reviewer that we have overstated the novelty of our work, and, in fact, we make very specific comments on the new contributions of our paper relative to the large previous literature on schooling. We are well aware of the literature cited above and many of these papers have little or nothing to do with quantifying the energetics of schooling. In addition, many of these papers rely on simple kinematic measurements which are unrelated to direct energetic measurements of energy use. To elaborate on this, we present the ‘Table R’ below which evaluates and compares each of the papers this reviewer cites above. The key message (as we wrote in the manuscript) is that none of the previous studies measured non-aerobic cost (and thus do not calculate the total energy expenditure (TEE), which we show to be substantial. In addition, many of these studies do not compare schools to individuals, do not quantify both energetics and kinematics, and do not study a wide speed range. Only 33% of previous studies used direct measurements of aerobic metabolic rate to compare the locomotion costs of fish schools and solitary individuals (an experimental control). We want to highlight that most of the citations in the reviewer’s comments are not about the kinematics or hydrodynamics of fish schooling energetics, although they provide peripheral information on fish schooling in general. We also provide an overview of the literature on this topic in our paper in the Journal of Experimental Biology (Zhang & Lauder 2023 doi:10.1242/jeb.245617) and do not wish to duplicate that discussion here. We summarized and cited the relevant papers about the energetics of fish schooling in Table 1.

Author response table 1.

Papers cited by Reviewer #2, and a summary of their contributions and approach.

References cited above:

Zuyev, G., & Belyayev, V. V. (1970). An experimental study of the swimming of fish in groups as exemplified by the horsemackerel [Trachurus mediterraneus ponticus Aleev]. J Ichthyol, 10, 545-549.

Weihs, D. (1973). Hydromechanics of fish schooling. Nature, 241(5387), 290-291.

Herskin, J., & Steffensen, J. F. (1998). Energy savings in sea bass swimming in a school: measurements of tail beat frequency and oxygen consumption at different swimming speeds. Journal of Fish Biology, 53(2), 366-376.

Marras, S., Killen, S. S., Lindström, J., McKenzie, D. J., Steffensen, J. F., & Domenici, P. (2015). Fish swimming in schools save energy regardless of their spatial position. Behavioral ecology and sociobiology, 69, 219-226.

Burgerhout, E., Tudorache, C., Brittijn, S. A., Palstra, A. P., Dirks, R. P., & van den Thillart, G. E. (2013). Schooling reduces energy consumption in swimming male European eels, Anguilla anguilla L. Journal of experimental marine biology and ecology, 448, 66-71.

Hemelrijk, C. K., Reid, D. A. P., Hildenbrandt, H., & Padding, J. T. (2015). The increased efficiency of fish swimming in a school. Fish and Fisheries, 16(3), 511-521.

Li, L., Nagy, M., Graving, J. M., Bak-Coleman, J., Xie, G., & Couzin, I. D. (2020). Vortex phase matching as a strategy for schooling in robots and in fish. Nature communications, 11(1), 5408.

Wiwchar, L. D., Gilbert, M. J., Kasurak, A. V., & Tierney, K. B. (2018). Schooling improves critical swimming performance in zebrafish (Danio rerio). Canadian Journal of Fisheries and Aquatic Sciences, 75(4), 653-661.

Verma, S., Novati, G., & Koumoutsakos, P. (2018). Efficient collective swimming by harnessing vortices through deep reinforcement learning. Proceedings of the National Academy of Sciences, 115(23), 5849-5854.

Ashraf, I., Bradshaw, H., Ha, T. T., Halloy, J., Godoy-Diana, R., & Thiria, B. (2017). Simple phalanx pattern leads to energy saving in cohesive fish schooling. Proceedings of the National Academy of Sciences, 114(36), 9599-9604.

Parker Jr, F. R. (1973). Reduced metabolic rates in fishes as a result of induced schooling. Transactions of the American Fisheries Society, 102(1), 125-131.

Itazawa, Y., & Takeda, T. (1978). Gas exchange in the carp gills in normoxic and hypoxic conditions. Respiration physiology, 35(3), 263-269.

Abrahams, M. V., & Colgan, P. W. (1985). Risk of predation, hydrodynamic efficiency and their influence on school structure. Environmental Biology of Fishes, 13, 195-202.

Davis, M. W., & Olla, B. L. (1992). The role of visual cues in the facilitation of growth in a schooling fish. Environmental biology of fishes, 34, 421-424.

Ross, R. M., Backman, T. W., & Limburg, K. E. (1992). Group-size-mediated metabolic rate reduction in American shad. Transactions of the American Fisheries Society, 121(3), 385-390.

Currier, M., Rouse, J., & Coughlin, D. J. (2021). Group swimming behaviour and energetics in bluegill Lepomis macrochirus and rainbow trout Oncorhynchus mykiss. Journal of Fish Biology, 98(4), 1105-1111.

Halsey, L. G., Wright, S., Racz, A., Metcalfe, J. D., & Killen, S. S. (2018). How does school size affect tail beat frequency in turbulent water?. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 218, 63-69.

Johansen, J. L., Vaknin, R., Steffensen, J. F., & Domenici, P. (2010). Kinematics and energetic benefits of schooling in the labriform fish, striped surfperch Embiotoca lateralis. Marine Ecology Progress Series, 420, 221-229.

3) In addition to the calming effect, measuring group oxygen consumption suffers from a number of problems as discussed in Herskin and Steffensen (1998) such as the fish volume to water volume ratio, which varies considerably when testing a school vs single individuals in the same tunnel and the problem of wall effect when using a small volume of water for accurate O2 measurements. Herskin and Steffensen (1998) circumvented these problems by measuring tailbeat frequencies of fish in a school and then calculating the MO2 of the corresponding tailbeat frequency in solitary fish in a swim tunnel. A similar approach was used by Johansen et al (2010), Marras et al (2015), Halsey et al (2018). However, It is not clear how these potential issues were dealt with here. Here, larger solitary D. aequipinnatus were used to increase the signal-to-noise ratio. However, using individuals of different sizes makes other variables not so directly comparable, including stress, energetics, and kinematics. (see comment 7 below).

Response: We acknowledge the great efforts made by previous studies to understand the energetics of fish schooling. These studies, as detailed in the table and elaborated in the response above (see comment 2) are very different from our current study. Our study achieved a direct comparison of energetics (including both aerobic and non-aerobic cost) and kinematics between solitary individuals and fish schools that has never been done before. Our detailed response to the supposed “calming effect” is given above.

As highlighted in the previous comments and opening statement, our current version has addressed the wall effect, tail beat frequency, and experimental and analytical efforts invested to directly compare the energetics between fish schools and solitary individuals. As readers can see in our comprehensive method section, achieving the direct comparison between solitary individuals and fish schools is not a trivial task. Now we want to elaborate on the role of kinematics as an indirect estimate of energetics. Our results here show that kinematic measurements of tail beat frequency are not reliable estimates of energetic cost, and the previous studies cited did not measure EPOC and those costs are substantial, especially as swimming speed increases. Fish in schools can save energy even when the tail beat frequency does not change (although school volume can change as we show). We elaborated (in great detail) on why kinematics does not always reflect on the energetics in the submitted version (see last paragraph of “Schooling dynamics and energy conservation” section). Somehow modeling what energy expenditure should be based only on tail kinematics is, in our view, a highly unreliable approach that has never been validated (e.g., fish use more than just tails for locomotion). Indeed, we believe that this is an inadequate substitute for direct energy measurements. We disagree that using slightly differently sized individuals is an issue since we recorded fish kinematics across all experiments and included the measurements of behaviour in our manuscript. Slightly altering the size of individual fish was done on purpose to provide a better ratio of respirometer volume to fish volume in the tests on individual fish, thus we regard this as a benefit of our approach and not a concern.

Finally, in another study of the collective behaviour of flying birds (Usherwood, J. R., Stavrou, M., Lowe, J. C., Roskilly, K. and Wilson, A. M. (2011). Flying in a flock comes at a cost in pigeons. Nature 474, 494-497), the authors observed that wing beat frequency can increase during flight with other birds. Hence, again, we cannot regard movement frequency of appendages as an adequate substitute for direct energetic measurements.

4) Svendsen et al (2016) provide guidelines for the ratio of fish volume to water volume in the respirometer. The ratio used here (2200) is much higher than that recommended. RFR values higher than 500 should be avoided in swim tunnel respirometry, according to Svendsen et al (2016).

Response: Thank you for raising this point. Please see the detailed responses above to the same comment above. We believe that our experimental setup and ratios are very much in line with those recommended, and represent a significant improvement on previous studies which use large ratios.

5) Lines 421-436: The same goes for wall effects. Presumably, using the same size swim tunnel, schooling fish were swimming much closer to the walls than solitary fish but this is not specifically quantified here in this paper. Lines 421-436 provide some information on the boundary layer (though wall effects are not just related by the boundary layer) and some qualitative assessment of school volume. However, no measurement of the distance between the fish and the wall is given.

Response: Please see the detailed responses above to the same comment. Specifically, we used the particle image velocimetry (PIV) system to measure the boundary layer (<2.5 mm at speeds > 2 BL s-1) and stated the parameters in the methods section of the Integrated Biomechanics & Bioenergetic Assessment System. We also state that the boundary layer is much thinner than the body width of the giant danio (~10 mm) so that the fish cannot effectively hide near the wall. Due to our PIV calibration, we are able to quantify flow near the wall.

Due to our video data obtained simultaneously with energetic measurements, we do not agree that fish were swimming closer to the wall in schools and also note that we took care to modify the typical respirometer to both ensure that flow across the cross-section did not provide any refuges and to quantify flow velocities in the chamber using particle image velocimetry. We do not believe that any previous experiments on schooling behaviour in fish have taken the same precautions.

6) The statistical tests used have a number of problems. Two-way ANOVA was based on school vs solitary and swimming speed. However, there are repeated measures at each speed and this needs to be dealt with. The degrees of freedom of one-way ANOVA and T-tests are not provided. These tests took into account five groups of fish vs. five solitary fish. The values of MO2 of each school are the result of the oxygen consumption of each fish, and therefore the test is comparing 5 individuals (i.e. an individual is the statistical unit) vs 5 schools (a school made out of 8 fish is the statistical unit). Therefore the test is comparing two different statistical units. One can see from the graphs that schooling MO2 tend to have a smaller SD than solitary data. This may well be due to the fact that schooling data are based on 5 points (five schools) and each point is the result of the MO2 of five fish, thereby reducing the variability compared to solitary fish. TBF, on the other hand, can be assigned to each fish even in a school, and therefore TBF of each fish could be compared by using a nested approach of schooling fish (nested within each school) vs solitary fish, but this is not the statistical procedure used in the present manuscript. The comparison between TBFs presumably is comparing 5 individuals vs all the fish in the schools (6x5=30 fish). However, the fish in the school are not independent measures.

Response: We cannot agree with this criticism, which may be based on this reviewer having seen a previous version of the manuscript. We did not use two-way ANOVA in this version. This version of the manuscript reported the statistical value based on a General Linear Model (see statistical section of the method). We are concerned that this reviewer did not in fact read either the Methods section or the Results section. In addition, it is hard to accept that, from examination of the data shown in Figure 3, there is not a clear and large difference between schooling and solitary locomotion, regardless of the statistical test used.

Meanwhile, the comments about the ‘repeated’ measures from one speed to the next are interesting, but we cannot agree. The ‘repeated’ measures are proper when one testing subject is assessed before and after treatment. Going from one speed to the next is not a treatment. Instead, the speed is a dependent and continuous variable. In our experimental design, the treatment is fish school, and the control is a solitary individual. Second, we never compared any of our dependent variables across different speeds within a school or within an individual. Instead, we compared schools and individuals at each speed. In this comparison, there are no ‘repeated’ measures. We agree with the reviewer that fish in the school are interacting (not independent). This is one more reason to support our approach of treating fish schools as a functional and statistical unit in our experiment design (more detailed responses are stated in the response to the comment above).

7) The size of solitary and schooling individuals appears to be quite different (solitary fish range 74-88 cm, schooling fish range 47-65 cm). While scaling laws can correct for this in the MO2, was this corrected for TBF and for speed in BL/s? Using BL/s for speed does not completely compensate for the differences in size.

Response: Our current version has provided justifications for not conducting scaling in the values of tail beat frequency. Our justification is “The mass scaling for tail beat frequency was not conducted because of the lack of data for D. aequipinnatus and its related species. Using the scaling exponent of distant species for mass scaling of tail beat frequency will introduce errors of unknown magnitude.”. Our current version also acknowledges the consideration about scaling as follows: “Fish of different size swimming at 1 BL s-1 will necessarily move at different Reynolds numbers, and hence the scaling of body size to swimming speed needs to be considered in future analyses of other species that differ in size”

Reviewer #3 (Public Review):

Summary:

Zhang and Lauder characterized both aerobic and anaerobic metabolic energy contributions in schools and solitary fishes in the Giant danio (Devario aequipinnatus) over a wide range of water velocities. By using a highly sophisticated respirometer system, the authors measure the aerobic metabolisms by oxygen uptake rate and the non-aerobic oxygen cost as excess post-exercise oxygen consumption (EPOC). With these data, the authors model the bioenergetic cost of schools and solitary fishes. The authors found that fish schools have a J-shaped metabolism-speed curve, with reduced total energy expenditure per tail beat compared to solitary fish. Fish in schools also recovered from exercise faster than solitary fish. Finally, the authors conclude that these energetic savings may underlie the prevalence of coordinated group locomotion in fish.

The conclusions of this paper are mostly well supported by data, but some aspects of methods and data acquisition need to be clarified and extended.

Response: Thank you for seeing the value of our study. We provided clarification of the data acquisition system with a new panel of pictures included in the supplemental material to show our experimental system. We understand that our methods have more details and justifications than the typical method sections. First, the details are to promote the reproducibility of the experiments. The justifications are the responses to reviewer 2, who reviewed our previous manuscript version and also posted the same critiques after we provided the justifications for the construction of the system and the data acquisition.

Strengths:

This work aims to understand whether animals moving through fluids (water in this case) exhibit highly coordinated group movement to reduce the cost of locomotion. By calculating the aerobic and anaerobic metabolic rates of school and solitary fishes, the authors provide direct energetic measurements that demonstrate the energy-saving benefits of coordinated group locomotion in fishes. The results of this paper show that fish schools save anaerobic energy and reduce the recovery time after peak swimming performance, suggesting that fishes can apport more energy to other fitness-related activities whether they move collectively through water.

Response: Thank you. We are excited to share our discoveries with the world.

Weaknesses:

Although the paper does have strengths in principle, the weakness of the paper is the method section. There is too much irrelevant information in the methods that sometimes is hard to follow for a researcher unfamiliar with the research topic. In addition, it was hard to imagine the experimental (respirometer) system used by the authors in the experiments; therefore, it would be beneficial for the article to include a diagram/scheme of that respiratory system.

Response: We agree with the reviewer and hence added the pictures of the experimental system in the supplementary materials (Fig. S4). We think pictures are more realistic to present the system than schematics. We also provide a picture of the system during the process of making the energetic measurements. It is to show the care went to ensure fish are not affected by any external stimulation other than the water velocity. The careful experimental protocol is very critical to reveal the concave upward shaped curve of bony fish schools that was never reported before. Many details in the methods have been included in response to Reviewer 2.

Reviewer #3 (Recommendations For The Authors):

Overall, this is a very interesting, well-written, and nice article. However, many times the method section looks like a discussion. Furthermore, the authors need to check the use of the word "which" throughout the text. I got the feeling that it is overused/misused sometimes.

Response: Thank you for the positive comments. The method is written in that way to address the concerns of Reviewer 2 who reviewed our previous versions. We corrected the overuse of ‘which’ throughout the manuscript.

Sweet spot

Goldilocks

Sweet spot not sweat spot

InterPersonal Computing

Sweet Spot

Beyond the threshold that is required

on 0:31-2:08 starts explaining that heat is actually more benificail for woman of the cold water bathtubs. Dr. stacy explains that the cold baths are "so cold that it causes the body to shut down". and says that the sweet spot for woman and cold baths is around 55 to 56 degrees farehnhites because they can still reap the benifitts as though they were doing ice water baths.

Four markets define the spectrum: a fragmented giant (Germany), a concentrated small market (Denmark), a state-owned dominance (Czech Republic), and a diversified market (Netherlands). Too fragmented and drivers juggle apps, cards and tariffs; too concentrated and prices drift while choice shrinks. The practical sweet spot is a few large players and a healthy fringe, backed by real roaming, transparent pricing, and reliable uptime.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

The manuscript "Drosophila Visuomotor Integration: An Integrative Model and Behavioral Evidence of Visual Efference Copy" provides an integrative model of the visuomotor control in Drosophila melanogaster. This model presents an experimentally derived model based on visually evoked wingbeat pattern recordings of three strategically selected visual stimulus types with well-established behavioral response characteristics. By testing variations of these models, the authors demonstrate that the virtual model behavior can recapitulate the recorded wing beat behavioral results and those recorded by others for these specific stimuli when presented individually. Yet, the novelty of this study and their model is that it allows predictions for natural visual scenes in which multiple visual stimuli occur simultaneously and may have opposite or enhancing effects on behavior. Testing three models that would allow interactions of these visual modalities, the authors show that using a visual efference copy signal allows visual streams to interact, replicating behavior recorded when multiple stimuli are presented simultaneously. Importantly, they validated the prediction of this model in real flies using magnetically tethered flies, e.g., presenting moving bars with varying backgrounds. In conclusion, the presented manuscript presents a commendable effort in developing and demonstrating the validity of a mixture model that allows predictions of the behavior of Drosophila in natural visual environments.

Strengths:

Overall, the manuscript is well-structured and clear in its presentation, and the modeling and experimental research are methodically conducted and illustrated in visually appealing and easy-to-understand figures and their captions.

The manuscript employs a thorough, logical approach, combining computational modeling with experimental behavioral validation using magnetically tethered flies. This iterative integration of simulation and empirical behavioral evidence enhances the credibility of the findings.

The associated code base is well documented and readily produces all figures in the document.

Suggestions:

However, while the experiments provide evidence for the use of a visual efference copy, the manuscript would be even more impressive if it presented specific predictions for the neural implementation or even neurophysiological data to support this model. Or, at the very least, a thorough discussion. Nonetheless, these models and validating behavioral experiments make this a valuable contribution to the field; it is well executed and addresses a significant gap in the modeling of fly behavior and holistic understanding of visuomotor behaviors.

We appreciate the reviewer’s thoughtful comments on the strengths and weaknesses of our manuscript. We agree that biophysically realistic model reflecting the structure of neural circuits as well as physiological data from them would be invaluable. However, we are currently unable to provide physiological evidence for EC-based suppression, nor provide circuit architecture for efference copy-based suppression of the stability circuit because the neural pathway underlying this behavior remains unidentified. Extensive recordings from the HS/VS system have revealed cell-type-specific motor-related inputs during both spontaneous and loom-evoked flight turns (Fenk et al., 2021; Kim et al., 2017, 2015). These studies predicted suppression of the optomotor stability response during such turns, and our new experiments confirmed this suppression specifically during loom-evoked turns (Figures 5, 6). However, these neurons are primarily involved in the head optomotor response, not the body optomotor response. We hope to extend our current model in future studies to incorporate more cellular-level detail, as the feedforward circuits underlying stability behavior become more clearly defined.

Here are a few points that should be addressed:

(1) The biomechanics block (Figure 2) should be elaborated on, to explain its relevance to behavior and relation to the underlying neural mechanisms.

We appreciate this suggestion. The mathematical representation of the biomechanics block has been developed by other groups in previous studies (Fry et al., 2003; Ristroph et al., 2010). We used exactly the same model, and its parameters were identical to those used in one of those studies (Fry et al., 2003; Ristroph et al., 2010), in which the parameters were estimated from the stabilizing response in response to magnetic “stumbling” pulses. In the previous version of the manuscript, we had a description of the biomechanics block in the Method section (see Equation 4). In response to the reviewer’s comment, we have made a few changes in Figure 2A and expanded the associated description in the main text, as follows.

(Line 160) “To test the orientation behavior of the model, we developed an expanded model, termed “virtual fly model” hereafter. In this model, we added a biomechanics block that transforms the torque response of the fly to the actual heading change according to kinematic parameters estimated previously (Michael H Dickinson, 2005; Ristroph et al., 2010) (Figure 2A, see Equation 4 in Methods and Movie S1). The virtual fly model, featuring position and velocity blocks that are conditioned on the type of the visual pattern, can now change its body orientation, simulating the visual orientation behavior of flies in the free flight condition.”

(2) It is unclear how the three integrative models with different strategies were chosen or what relevance they have to neural implementation. This should be explained and/or addressed.

Thank you for this valuable comment. We selected the three models based on previous studies investigating visuomotor integration across multiple species, under conditions where multiple sensory cues are presented simultaneously.

The addition-only model represents the simplest hypothesis, analogous to the “additive model” proposed by Tom Collett in his 1980 study (Collett, 1980). We used this model as a baseline to illustrate behavior in the absence of any efference copy mechanism. Notably, some modeling studies have proposed linear (additive) integration for multimodal sensory cues at the behavioral level (Liu et al., 2023; Van der Stoep et al., 2021). However, experimental evidence demonstrating strictly linear integration—either behaviorally or physiologically—remains limited. In our study, new data (Figure 5) show that bar-evoked and background movement-evoked locomotor responses are combined linearly, supporting the addition-only model.

The graded efference copy model has been most clearly demonstrated in the cerebellum-like circuit of Mormyrid fish during electrosensation (Bell, 1981; Kennedy et al., 2014). In this system, the efference copy signal forms a negative image of the predicted reafferent input and undergoes plastic changes as the environment changes—an idea that inspired our modifiable efference copy model (Figure 4–figure supplement 1). The all-or-none efference copy model is exemplified in the sensory systems of smaller organisms, such as the auditory neurons of crickets during stridulation (Poulet and Hedwig, 2006). Notably, in crickets, the motor-related input is referred to as corollary discharge rather than efference copy. Typically, “efference copy” refers to a graded, subtractive motor-related signal, while “corollary discharge” denotes an all-or-none signal, both counteracting the sensory consequences of self-generated actions. In this manuscript, we use the term efference copy more broadly, encompassing both types of motor-related feedback signals (Sommer and Wurtz, 2008).

In response to this comment, we have made the following changes in the main text to enhance its accessibility to general readers.

(Line#268) “This integration problem has been studied across animal sensory systems, typically by analyzing motor-related signals observed in sensory neurons (Bell, 1981; Collett, 1980; Kim et al., 2017; Poulet and Hedwig, 2006). Building on the results of these studies, we developed three integrative models. The first model, termed the “addition-only model”, assumes that the outputs of the object (bar) and the background (grating) response circuits are summed to control the flight orientation (Figure 4B, see Equation 14 in Methods).”

(Line#272) “In the second and third models, an EC is used to set priorities between different visuomotor circuits (Figure 4C,D). In particular, the EC is derived from the object-induced motor command and sent to the object response system to nullify visual input associated with the object-evoked turn (Bell, 1981; Collett, 1980; Poulet and Hedwig, 2006). These motor-related inputs fully suppress sensory processing in some systems (Poulet and Hedwig, 2006), whereas in others they selectively counteract only the undesirable components of the sensory feedback (Bell, 1981; Kennedy et al., 2014).”

(3) There should be a discussion of how the visual efference could be represented in the biological model and an evaluation of the plausibility and alternatives.

Thank you for this helpful comment. We have now added the following discussion to share our perspective on the circuit-level implementation of the visual efference copy in Drosophila.

(Line#481) “Efference copy in Drosophila vision

Under natural conditions, various visual features in the environment may concurrently activate multiple motor programs. Because these may interfere with one another, it is crucial for the central brain to coordinate between the motor signals originating from different sensory circuits. Among such coordination mechanisms, the EC mechanisms were hypothesized to counteract so-called reafferent visual input, those caused specifically by self-movement (Collett, 1980; von Holst and Mittelstaedt, 1950). Recent studies reported such EC-like signals in Drosophila visual neurons during spontaneous as well as loom-evoked flight turns (Fenk et al., 2021; Kim et al., 2017, 2015). One type of EC-like signals were identified in a group of wide-field visual motion-sensing neurons that were shown to control the neck movement for the gaze stability (Kim et al., 2017). The EC-like signals in these cells were bidirectional depending on the direction of flight turns, and their amplitudes were quantitatively tuned to those of the expected visual input across cell types. Although amplitude varies among cell types, it remains inconclusive whether it also varies within a given cell type to match the amplitude of expected visual feedback, thereby implementing the graded EC signal. A more recent study examined EC-like signal amplitude in the same visual neurons for loom-evoked turns, across events (Fenk et al., 2021). Although the result showed a strong correlation between wing response and the EC-like inputs, the authors pointed that this apparent correlation could stem from noisy measurement of all-or-none motor-related inputs.

Thus, these studies did not completely disambiguate between graded vs. all-or-none EC signaling. Another type of EC-like signals observed in the visual circuit tuned to a moving spot exhibited characteristics consistent with all-or-none EC. That is, it entirely suppressed visual signaling, irrespective of the direction of the self-generated turn (Kim et al., 2015; Turner et al., 2022). 

Efference-copy (EC)–like signals have been reported in several Drosophila visual circuits, yet their behavioral role remains unclear. Indirect evidence comes from a behavioral study showing that the dynamics of spontaneously generated flight turns were unaffected by unexpected background motion (Bender and Dickinson, 2006a). Likewise, our behavioral experiments showed that, during loom-evoked turns, responses to background motion are suppressed in an all-or-none manner (Figures 6 and 7). Consistent with this, motor-related inputs recorded in visual neurons exhibit nearly identical dynamics during spontaneous and loom-evoked turns (Fenk et al., 2021). Together, these behavioral and physiological parallels support the idea that a common efference-copy mechanism operates during both spontaneous and loom-evoked flight turns.

Unlike loom-evoked turns, bar-evoked turn dynamics changed in the presence of moving backgrounds (Figure 5), a result compatible with both the addition-only and graded EC models. However, when the static background was updated just before a bar-evoked turn—thereby altering the amplitude of optic flow—the turn dynamics remained unaffected (Figures 5 and 7), clearly contradicting the addition-only model. Thus, the graded EC model is the only one consistent with both findings. If a graded EC mechanism were truly at work, however, an unexpected background change should have modified turn dynamics because of the mismatch between expected and actual visual feedback (Figure 4–figure supplement 1)—yet we detected no such effect at any time scale examined (Figure 7–figure supplement 1). This mismatch would be ignored only if the amplitude of the graded EC adapted to environmental changes almost instantaneously—a mechanism that seems improbable given the limited computational capacity of the Drosophila brain. In electric fish, for example, comparable adjustments take more than 10 minutes (Bell, 1981; Muller et al., 2019). Further investigation is needed to clarify how reorienting flies ignore optic flow generated by static backgrounds, potentially by engaging EC mechanisms not captured by the models tested in this study.

Why would Drosophila rely on the all-or-none EC mechanism instead of the graded one for loom-evoked turns? A graded EC must be adjusted adaptively depending on the environment, as the amplitude of visual feedback varies with both the dynamics of self-generated movement and environmental conditions (e.g., empty vs. cluttered visual backgrounds) (Figure 4—figure supplement 1). Recent studies on electric fish have suggested that a large array of neurons in a multi-layer network is crucial for generating a modifiable efference copy signal matched to the current environment (Muller et al., 2019). Given their small-sized brain, flies might opt for a more economical design for suppressing unwanted visual inputs regardless of the visual environment. Circuits mediating such a type of EC were identified in the cricket auditory system during stridulation (Poulet and Hedwig, 2006), for example. Our study strongly suggests the existence of a similar circuit in the Drosophila visual system. 

We tested the hypothesis that efference-copy (EC) signals guide action selection by suppressing specific visuomotor reflexes when multiple visual features compete. An alternative motif with a similar function is mutual inhibition between motor pathways (Edwards, 1991; Mysore and Kothari, 2020). In Drosophila, descending neurons form dense lateral connections (Braun et al., 2024), offering a substrate for such competitive interactions. Determining whether—and how—EC and mutual inhibition operate will require recordings from the neurons that ensure visual stability, which remain unidentified. Mapping these pathways and assessing how they are modulated by visual and behavioral context are important goals for future work.”

Reviewer #2 (Public Review):

It has been widely proposed that the neural circuit uses a copy of motor command, an efference copy, to cancel out self-generated sensory stimuli so that intended movement is not disturbed by the reafferent sensory inputs. However, how quantitatively such an efference copy suppresses sensory inputs is unknown. Here, Canelo et al. tried to demonstrate that an efference copy operates in an all-or-none manner and that its amplitude is independent of the amplitude of the sensory signal to be suppressed. Understanding the nature of such an efference copy is important because animals generally move during sensory processing, and the movement would devastatingly distort that without a proper correction. The manuscript is concise and written very clearly. However, experiments do not directly demonstrate if the animal indeed uses an efference copy in the presented visual paradigms and if such a signal is indeed non-scaled. As it is, it is not clear if the suppression of behavioral response to the visual background is due to the act of an efference copy (a copy of motor command) or due to an alternative, more global inhibitory mechanism, such as feedforward inhibition at the sensory level or attentional modulation. To directly uncover the nature of an efference copy, physiological experiments are necessary. If that is technically challenging, it requires finding a behavioral signature that unambiguously reports a (copy of) motor command and quantifying the nature of that behavior.

We thank the reviewer for this insightful and constructive comment. We agree that our current behavioral evidence does not directly identify the underlying circuit mechanism, and that direct recordings from visual neurons modulated by an efference copy would be critical for distinguishing between potential mechanisms.

A prerequisite for such physiological investigations would be the identification of both (1) the feedforward neurons directly involved in the optomotor response, and (2) the neurons conveying motor-related signals to the optomotor circuit. Despite efforts by several research groups, the location of the feedforward circuit mediating the optomotor response remains elusive. This limitation has prevented us from obtaining direct cellular evidence of flight turn-associated suppression of optomotor signaling.

In light of the reviewer’s suggestion, we expanded our investigation to strengthen the behavioral evidence for efference copy (EC) mechanisms. In addition to our earlier experiments involving unexpected changes in the static background, we examined how object-evoked flight turns influence the optomotor stability reflex and vice versa (Figures 5 and 6). To quantify the interaction between different visuomotor behaviors, we systematically varied the temporal relationship between two types of visual motion—loom versus moving background, or moving bar versus moving background—and measured the resulting behavioral responses.

Our findings support pattern- and time-specific suppressive mechanisms acting between flight turns associated with the different visual patterns. Specifically:

The responses to a moving bar and a moving background add linearly, even when presented in close temporal proximity.

Loom-evoked turns and the optomotor stability reflex mutually suppress each other in a time-specific manner.

For both loom- and moving bar-evoked flight turns, changes in the static background had no measurable effect on the dynamics of the object-evoked responses.

These results provide a detailed behavioral characterization of a suppressive interaction between distinct visuomotor responses. This, in turn, offers correlative evidence supporting the involvement of an efference copy-like mechanism acting on the visual system. While similar efference copy mechanisms have been documented in other parts of the visual system, we acknowledge that our findings do not exclude alternative explanations. In particular, it is still possible that lateral inhibition within the central brain or ventral nerve cord contributes to the suppression we observed.

Ultimately, definitive proof will require identifying the specific neurons that convey efference copy signals and demonstrating that silencing these neurons abolishes the behavioral suppression. Until such experiments are feasible, our behavioral approach provides an important contribution toward understanding the nature of sensorimotor integration in this system.

Reviewer #3 (Public Review):

Summary:

Canelo et al. used a combination of mathematical modeling and behavioral experiments to ask whether flies use an all-or-none EC model or a graded EC model (in which the turn amplitude is modulated by wide-field optic flow). Particularly, the authors focus on the bar-ground discrimination problem, which has received significant attention in flies over the last 50-60 years. First, they use a model by Poggio and Reichardt to model flight response to moving small-field bars and spots and wide-field gratings. They then simulate this model and compare simulation results to flight responses in a yaw-free tether and find generally good agreement. They then ask how flies may do bar-background discrimination (i.e. complex visual environment) and invoke different EC models and an additive model (balancing torque production due to background and bar movement). Using behavioral experiments and simulation supports the notion that flies use an all-or-none EC since flight turns are not influenced by the background optic flow. While the study is interesting, there are major issues with the conceptual framework.

Strengths:

They ask a significant question related to efference copies during volitional movement.

The methods are well detailed and the data (and statistics) are presented clearly.

The integration of behavioral experiments and mathematical modeling of flight behavior.

The figures are overall very clear and salient.

Weaknesses:

Omission of saccades: While the authors ask a significant question related to the mechanism of bar-ground discrimination, they fail to integrate an essential component of the Drosophila visuomotor responses: saccades. Indeed, the Poggio and Reichardt model, which was developed almost 50 years ago, while appropriate to study body-fixed flight, has a severe limitation: it does not consider saccades. The authors identify this major issue in the Discussion by citing a recent switched, integrate-and-fire model (Mongeau & Frye, 2017). The authors admit that they "approximated" this model as a smooth pursuit movement. However, I disagree that it is an approximation; rather it is an omission of a motor program that is critical for volitional visuomotor behavior. Indeed, saccades are the main strategy by which Drosophila turn in free flight and prior to landing on an object (i.e. akin to a bar), as reported by the Dickinson group (Censi et al., van Breugel & Dickinson [not cited]). Flies appear to solve the bar-ground discrimination problem by switching between smooth movement and saccades (Mongeau & Frye, 2017; Mongeau et al., 2019 [not cited]). Thus, ignoring saccades is a major issue with the current study as it makes their model disconnected from flight behavior, which has been studied in a more natural context since the work of Poggio.

Thank you for this helpful comment. We agree that including saccadic turns is essential and qualitatively improves the model. In the revised manuscript, we therefore expanded our bar-tracking model to incorporate an integrate-and-saccade strategy, now presented in Figure 2—figure supplement

The manuscript now introduces this result as follows:

(Line#190) “Finally, one important locomotion dynamics that a flying Drosophila exhibits while tracking an object is a rapid orientation change, called a “saccade” (Breugel and Dickinson, 2012; Censi et al., 2013; Heisenberg and Wolf, 1979). For example, while tracking a slowly moving bar, flies perform relatively straight flights interspersed with saccadic flight turns (Collett and Land, 1975; Mongeau and Frye, 2017). During this behavior, it has been proposed that visual circuits compute an integrated error of the bar position with respect to the frontal midline and triggers a saccadic turn toward the bar when the integrated value reaches a threshold (Frighetto and Frye, 2023; Mongeau et al., 2019; Mongeau and Frye, 2017). We expanded our bar fixation model to incorporate this behavioral strategy (Figure 2--figure supplement 2). The overall structure of the modified model is akin to the one proposed in a previous study (Mongeau and Frye, 2017), and the amplitude of a saccadic turn was determined by the sum of the position and velocity functions (Figure 2--figure supplement 2A; see Equation 13 in Methods). When simulated, our model successfully reproduced experimental observations of saccade dynamics across different object velocities (Figure 2--figure supplement 2B-D) (Mongeau and Frye, 2017). Together, our models faithfully recapitulated the results of previous behavioral observations in response to singly presented visual patterns (Collett, 1980; Götz, 1987; H. Kim et al., 2023; Maimon et al., 2008; Mongeau and Frye, 2017).”

Apart from Figures 1 and 2, most of our data—whether from simulations or behavioral experiments—use brief visual patterns lasting 200 ms or less. These stimuli trigger a single, rapid orientation change reminiscent of a saccadic flight turn. In this part of the paper, we essentially have examined how multiple visuomotor pathways interact to determine the direction of object-evoked turns when several visual patterns occur simultaneously.

Critically, recent work showed that a group of columnar neurons (T3) appear specialized for saccadic bar tracking through integrate-and-fire computations, supporting the notion of parallel visual circuits for saccades and smooth movement (Frighetto & Frye, 2023 [not cited]).

Thanks for bringing up this critical issue. We have now added this paper in the following part of the manuscript.

(Line#193) “During this behavior, it has been proposed that visual circuits compute an integrated error of the horizontal bar position with respect to the frontal midline and triggers a saccadic turn toward the bar when the integrated value reaches a threshold (Frighetto and Frye, 2023; Mongeau and Frye, 2017).”

(Line#462) “Visual systems extract features from the environment by calculating spatiotemporal relationships of neural activities within an array of photoreceptors. In Drosophila, these calculations occur initially on a local scale in the peripheral layers of the optic lobe (Frighetto and Frye, 2023; Gruntman et al., 2018; Ketkar et al., 2020).”

A major theme of this work is bar fixation, yet recent work showed that in the presence of proprioceptive feedback, flies do not actually center a bar (Rimniceanu & Frye, 2023). Furthermore, the same study found that yaw-free flies do not smoothly track bars but instead generate saccades. Thus prior work is in direct conflict with the work here. This is a major issue that requires more engagement by the authors.

Thank you for your thoughtful comments and for drawing our attention to this important paper. In our experiments, bar fixation on oscillating vertical objects emerges during the “alignment” phase of the magneto-tether protocol. The pattern movement dynamics was similar those used by Rimniceanu & Frye (2023), yet the two studies differ in a key respect: Rimniceanu & Frye employed a motion-defined bar, whereas we presented a dark vertical bar against a uniform or random-dot background. The alignment success rate—defined as the proportion of trials in which the fly’s body angle is within ±25° of the target—was about 50 % (data not shown). Our alignment pattern consisted of three vertical stripes spanning ~40° horizontally; when we replaced it with a single, narrower stripe, the success rate was lowered (data not shown). These observations suggest that bar fixation in the magnetically tethered assay is less robust than in the rigid-tethered assay, although flies still orient toward highly salient vertical objects.

We also observed that bar-evoked turns were elicited more reliably when the bar moved rapidly (45° in 200 ms) in the magneto-tether assay, although the turn magnitude was significantly smaller than the actual bar displacement (Figure 3).

In response to the reviewer’s comment, we now added the following description in the paper regarding the bar fixation behavior, citing Rimniceanu&Frye 2023.

(Line#239) “Another potential explanation arises from recent studies demonstrating that proprioceptive feedback provided during flight turns in a magnetically tethered assay strongly dampens the amplitude of wing and head responses (Cellini and Mongeau, 2022; Rimniceanu et al., 2023).”

Relevance of the EC model: EC-related studies by the authors linked cancellation signals to saccades (Kim et al, 2014 & 2017). Puzzlingly, the authors applied an EC model to smooth movement, when the authors' own work showed that smooth course stabilizing flight turns do not receive cancellation signals (Fenk et al., 2021). Thus, in Fig. 4C, based on the state of the field, the efference copy signal should originate from the torque commands to initiate saccades, and not from torque to generate smooth movement. As this group previously showed, cancellation signals are quantitatively tuned to that of the expected visual input during saccades. Importantly, this tuning would be to the anticipated saccadic turn optic flow. Thus the authors' results supporting an all-or-none model appear in direct conflict with the author's previous work. Further, the addition-only model is not particularly helpful as it has been already refuted by behavioral experiments (Rimneceanu & Frye, Mongeau & Frye).

Thank you for this constructive comment. Efference copy is best established for brief, discrete actions like flight saccades. While motor-related modulation of visual processing has been reported across short- and long-duration behaviours (Chiappe et al., 2010; Fujiwara et al., 2017; Kim et al., 2015, 2017; Maimon et al., 2010; Turner et al., 2022), only flight saccade-associated signals exhibit the temporal profile appropriate to cancel reafferent input. However, von Holst & Mittelstaedt (1950) originally formulated efference copy to explain the smooth optomotor response of hoverflies. In HS/VS recordings in previous studies, however, we could not detect membrane-potential changes tied to baseline wing-beat amplitude (data not shown), but further work is needed. 

Note that visually evoked flight turns analyzed in this paper have relatively fast dynamics. Fenk et al. (2021) showed that HS cells carry EC-like motor signals during both loom-evoked turns and spontaneous saccades. Building on this, we tested whether object-evoked rapid turns modulate other visuomotor pathways. Although Fenk et al. also found that optomotor turns lack motor input to HS cells, the authors did not test whether the optomotor pathway suppresses other reflexes, such as loom-evoked turns. Our new behavioral data (Figure 6) show that optomotor turns indeed suppress loom-evoked turns, suggesting a potential EC signal arising from the optomotor pathway that inhibits loom-responsive visual neurons.

In Kim et al. (2017), the authors argued that HS/VS neurons receive a “quantitatively tuned” efference copy that varies across cell types: yaw-sensitive LPTCs are strongly suppressed, roll-sensitive cells receive intermediate input, and pitch-sensitive cells receive little or none. We also showed that when the amplitude of ongoing visual drive changes, the amplitude of saccade-related potentials (SRPs) scales linearly. This proportionality does not imply a genuinely graded EC, however, because SRP amplitude could vary solely through changes in driving force (Vm – Vrest) with a fixed EC conductance. Crucially, SRPs do not fully suppress feed-forward visual signalling, arguing against an all-or-none EC mechanism.

How, then, can the cellular and behavioural data be reconciled? Silencing HS/VS neurons—or their primary inputs, the T4/T5 neurons—does not markedly diminish the optomotor response in flight (Fenk et al., 2014; Kim et al., 2017), indicating the presence of additional, as-yet-unidentified pathways.

Physiological recordings from other visual neurons that drive the optomotor response in flying Drosophila are therefore needed to determine how strongly they are suppressed during loom-evoked turns.

Behavioral evidence for all-or-none EC model: The authors state "unless the stability reflex is suppressed during the flies' object evoked turns, the turns should slow down more strongly with the dense background than the sparse one". This hypothesis is based on the fact that the optomotor response magnitude is larger with a denser background, as would be predicted by an EMD model (because there are more pixels projected onto the eye). However, based on the authors' previous work, the EC should be tuned to optic flow and thus the turning velocity (or amplitude). Thus the EC need not be directly tied to the background statistics, as they claim. For instance, I think it would be important to distinguish whether a mismatch in reafferent velocity (optic flow) links to distinct turn velocities (and thus position). This would require moving the background at different velocities (co- and anti-directionally) at the onset of bar motion. Overall, there are alternative hypotheses here that need to be discussed and more fully explored (as presented by Bender & Dickinson and in work by the Maimon group).

We appreciate the reviewer’s important suggestion. In response, we performed the recommended experiment. In Figures 5 and 6 of the revised manuscript, we now present how bar- or loom-evoked flight turns affect the response to a moving background pattern. These experiments revealed that bar-evoked turns do not suppress the optic flow response, whereas loom-evoked turns strongly suppress it. Specifically, when background motion began 100 ms after the onset of loom expansion, the response to the background was significantly suppressed. Although weak residual responses to the background motion were observed in this case, this could be due to background motion occurring outside of the suppression interval, which may correspond in duration to the duration of flight turns (Figure 6C,D). 

The lack of suppression of the optic flow response during and after bar-evoked turns appears to suggest that the responses are added linearly (Figure 5), seemingly contradicting the lack of dynamic change when the background dot density was altered (Figure 7, Figure 7–figure supplement 1). That is, the experimental result in Figure 5 supports either an addition-only or a graded efference copy (EC) model. However, the result in Figure 7 supports an all-or-none EC model. If a graded EC were used, the amplitude of the EC should be updated almost instantaneously when the static background changes.

Another possibility is that the optic flow during self-generated turns in a static background is extremely weak compared to the optic flow input generated by physically moving the pattern, perhaps due to the rapid nature of head movements. Indeed, detailed kinematic analysis of head movement during spontaneous saccades in blow flies revealed that the head reaches the target angle before the body completes the orientation change, making the effective speed of reafferent optic flow higher than the speed of body rotation (Hateren and Schilstra, 1999). To test these hypotheses, further experiments will be needed for bar-evoked flight turns.

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The reviewers identified two key revisions that could improve the assessment of the paper:

(1) Consideration of saccades within the model framework (outlined by reviewer 3).

(2) Addition of physiology data to support the conclusions of the paper (outlined by reviewer 2). If this is not feasible within the timescale of revisions, the paper would need to be revised to clarify that the model leads to a hypothesis that would need to be tested with future physiology experiments.

Thank you for these comments.

Regarding revision point #1, we have added Figure 2–figure supplement 2, where we incorporated our position-velocity model (estimated in Figure 1) into the framework of the integrate-and-saccade model. A detailed description of this model is now provided in the main text (Lines 190–203).

For revision point #2, obtaining electrophysiological evidence for efference copy remains challenging, as neither the visual neurons nor the efference-copy neuron has been identified for the wing optomotor response. As suggested by the reviewers, we have revised the title of the paper to reduce emphasis on efference copy and have noted electrophysiological recordings as a direction for future work.

old title: A visual efference copy-based navigation algorithm in Drosophila for complex visual environments

new title: Integrative models of visually guided steering in Drosophila

Specific recommendations are detailed below.

Reviewer #2 (Recommendations For The Authors):

To directly demonstrate if an efference copy is non-scaled, the following experiments can be helpful: record from HS/VS cells and examine the relation between the amplitude of the succade-suppression signal vs. succade amplitude.

Thanks for raising this important point. We previously carried out the suggested analysis for loom-evoked saccades in Fenk et al. (2021). There, significant correlations emerged between wing-response amplitude and saccade-related potentials (Figures 2F and 3C). However, we did not interpret the strong correlation (r ≈ 0.8) as evidence for a graded efference copy, because the amplitude of saccade-related potentials appeared to be bimodal. Upon presentation of the looming stimulus, flies either executed large evasive turns or showed minimal changes in wing-stroke amplitude. Large wing responses were accompanied by strong, saturated suppression of HS-cell membrane potential, whereas trials without wing responses produced only weak modulations—reflected in the bimodal distribution of saccade-related potential amplitudes (Figure 3C). 

Importantly, in rigidly tethered preparations—where these potentials are typically measured—the absence of proprioceptive feedback can itself drive wingbeat amplitudes to saturation during saccades. We therefore reasoned that the lack of intermediate-sized flight saccades would naturally yield correspondingly saturated saccade-related potentials, even if a graded EC system is in play. 

In Kim et al. (2017), we also performed a comprehensive analysis of spontaneous saccade-related potentials across all HS/VS cell types. When we later examined the relationship between saccade amplitude and the corresponding saccade-related potentials in each cell type, we could not find any statistically significant correlation (unpublished data).

measure how much a weak visual stimulus and a strong visual stimulus are suppressed by the suppression signal. If the signal is non-scaled, visual stimuli should always be suppressed independently of their intensities.

Thank you for this important suggestion. As mentioned in our response to the previous comment, we believe it is not feasible to record from neurons responsible for the body optomotor response at this point, as their identity remains unknown. Regarding the HS/VS cells, our previous study showed that HS cells are not always fully suppressed. The changes in saccade-related potential amplitude can be described as a linear function of the pre-saccadic visually-evoked membrane potential (Figure 7 in Kim et al., 2017). 

As suggested by Fenk et al. 2014 (doi: 10.1016/j.cub.2014.10.042), HS cells might also be responsive to a moving bar. If that is the case, and if you present a bar and background (either sparse or dense) in a closed-loop manner to a head-fixed fly, HS cells might be sensitive only to the bar but not to the background (independently of the density).

Thanks for pointing out this important issue. HS cells indeed respond strongly to the horizontal movement of a vertical bar, as expected given that their receptive fields are formed by the integration of local optic flow vectors. In one of our previous studies (Supplemental Figure 1 in Kim et al., 2015), we showed that the response amplitude to a single vertical bar is roughly equivalent to that elicited by a vertical grating composed of 12 bars of the same size. Therefore, we believe that HS cells are likely to contribute to the head response to a moving vertical bar. In a body-fixed flight simulator, HS cells would respond only to the bar if the bar runs in a closed loop with a static background. In this scenario, HS cells are likely to play a role in the head optomotor response.

Note also that the role of HS cells in the wing optomotor response remains unresolved. Unilateral activation of HS cells has been shown to elicit locomotor turns in walking Drosophila (Fujiwara et al., 2017), as well as in flying individuals (unpublished data from our lab). However, a previous study also showed that strong silencing of HS/VS cells significantly reduced the head optomotor response, but not the wing optomotor response (Kim et al., 2017).

If neurophysiology is technically challenging, an alternative way might pay attention to a head movement that exclusively follows the background (Fox et al., 2014 (doi: 10.1242/jeb.080192)). Because HS cells are thought to promote head rotation to background motion, a non-scaled suppression signal on HS cells would always suppress the head rotation independently of the background density.

Thanks for this helpful comment. We have analyzed head movements during bar-evoked flight turns (Figure 7–figure supplement 1B) and found no significant changes across different background dot densities. We think that this might suggest that HS cells are unlikely to receive suppressive inputs during bar-evoked turns, akin to the lack of modulation during optomotor turns.

Another way to separate a potential efference copy from other mechanisms (more global inhibition) is the directionality. A global inhibition would suppress the response to the background even if the background moves in the same direction as self-motion, but the efference copy would not.

Thanks for this important point. In Heisenberg and Wolf, 1979, it was proposed that modulation might be bidirectional, with behavioral effects observed only for perturbations in the “unexpected” direction. In our new data on loom-evoked turns (Figure 6), the suppression appears equally strong for background motion in either direction, supporting an all-or-none suppression mechanism.

Besides, in general, it is unclear if you think an efference copy operates both in smooth pursuits and saccades or if such a signal is only present during saccades. Your previous neurophysiological work supports the latter. Are your behavioral results consistent with the previous saccade suppression idea, or do you propose a new type of efference copy that also operates in smooth pursuits?

Thanks for raising this important point. von Holst and Mittelstaedt (1950) originally introduced the concept of efference copy to explain the smooth optomotor response. We previously analyzed electrophysiological recordings from HS cells for membrane-potential changes associated with slow deviations in wing-steering angle but found none. However, this negative result does not entirely rule out modulation of visual processing during smooth flight turns, given the slow drift in membrane potential observed in most whole-cell recordings.

In this study, We examined only the interactions among visuomotor pathways during these rapid flight turns as the dynamics of visually evoked turns are almost as rapid as spontaneous saccades. Our data reveal that interactions between distinct visuomotor reflexes are more diverse than previously appreciated.

Minor comments:

Line 108, 109: match the description between here and the labels in Fig. 1F.

Thank you for indicating this issue. We have defined the general equation to obtain the position and velocity components in the main text lines 108,109, but due to a slight asymmetry in the data (Fig. 1E) we used the approach indicated in Fig. 1F. and explained in lines 113-117.

Fig.1 F: If the position-dependent component is due to fatigue, the tuning curve's shape is likely changed (shrunk or extended) depending on the stimulus speed. How can you generalize the tuning curve shown here? Does the result hold even if the stimulus speed/contrast/spatial frequency is changed?

We appreciate this indication. We believed that fatigue may be the reason why the wing response to the grating stimulus showed that significant decay (Fig. 1E). As you mention, the stimulus speed would increase the amplitude of the fly’s response up to a saturation point. We addressed this in our model by multiplying the derived value by the angular velocity of the grating.

Regarding the contrast, and spatial frequency we did not test it experimentally, instead, we simulated our model for changing visual feedback (Fig. 4A, B), which can be seen as increasing/decreasing contrast of a grating. An increase in the contrast would increase the response of the fly to the grating and so will contribute to dampening the response to the foreground object (Fig. 4C).

Line 233-255: Here, the description sounds like you will consider several parallel objects (e.g., two stripes) in the visual field instead of the combination of the figure and background (which is referred to in the following paragraph).

Thank you for pointing it out. Indeed it was slightly ambiguous. We have addressed this by explaining the specific situation of a combination of an object and the background in lines 231-233.

Figure 6C: you kept the foreground visual field between sparse and dense random dot backgrounds to keep the bar's saliency. Is it sure that this does not influence the difference in the fly's response to these two backgrounds (in Figure 6B)?

This is a good point that we have also discussed internally. We also carried out similar experiments with a fully covered background and found no significant differences (Figure 7–figure supplement 1).

Reviewer #3 (Recommendations For The Authors):

Identify and analyze flight saccade dynamics in the raw trajectories (e.g., Fig. 3B). There should be some since the bar is near the 'sweet spot' for triggering saccades (see Mongeau & Frye, 2017).

Thank you for bringing up this interesting point. In previous work, it was reported that the fly fixated on a vertical bar through saccadic turns rather than smooth-tracking (Mongeau & Frye, 2017). When the bar width was thin (<15 deg) there was barely one saccade per second (Mongeau & Frye, 2017, Fig. 4). In our magno tether essay (Fig. 3A, B) the object width was 11.25 degrees, and the object moved for a short time window, and so the fly only generated the saccade related to the onset of the object. It could not be considered as a saccade some small turns of a few degrees that are likely related to small perturbations in comparison to those previously reported (Mongeau & Frye, 2017). Additionally, in our protocol (Fig. 3A) from onset time (‘go’ mark), only a single object moved, within an empty background, so in principle there is no trigger for a switch to a smooth movement. We addressed this in lines x-x.

Consider updating the Poggio model with flight saccades (switched, integrate-and-fire).

We appreciate this suggestion. Following previous work (Mongeau et al., 2017), we expanded our model to include a saccade mechanism: the torque produced by the summed position- and velocity-dependent components is now replaced by an integrate-and-fire saccade (Figure 2—figure supplement 2). We optimized the saccade interval and amplitude so that both vary linearly with stimulus amplitude and faithfully reproduce the kinematic properties reported previously (Mongeau et al., 2017).  

Please engage more with the literature, especially work that directly conflicts with your conclusions (see above). Also, highly relevant work by Bender & Dickinson was not sufficiently discussed. Spot results presented in Fig. 3 should be contextualized in light of the work of Mongeau et al., 2019, who performed similar experiments and identified a switch in saccade valence.

We appreciate your pointing out the relevant previous work. We have added references to the following papers and tried to describe the relationship between our data and previous ones.

Bender & Dickinson 2006

(Line#162) “This simulation experiment is reminiscent of the magnetically tethered flight assay, where a flying fly remains fixed at a position but is free to rotate around its yaw axis (Bender and Dickinson, 2006b; Cellini et al., 2022; G. Kim et al., 2023; Mongeau and Frye, 2017).”

(Line#218) “We tested the predictions of our models with flies flying in an environment similar to that used in the simulation (Figure 3A). A fly was tethered to a short steel pin positioned vertically at the center of a vertically oriented magnetic field, allowing it to rotate around its yaw axis with minimal friction (Bender and Dickinson, 2006b; Cellini et al., 2022; G. Kim et al., 2023).”

(Line#238) “To determine if our assay imposes additional friction compared to other assays used in previous studies, we analyzed the dynamics of spontaneous saccades during the “freeze” phase (Figure 3–figure supplement 1A). We found their duration and amplitude to be within the range reported previously (Bender and Dickinson, 2006b; Mongeau and Frye, 2017) (Figure 3–figure supplement 1B-D). 

Mongeau et al., 2019

(Line#196) “During this behavior, it has been proposed that visual circuits compute an integrated error of the bar position with respect to the frontal midline and triggers a saccadic turn toward the bar when the integrated value reaches a threshold (Frighetto and Frye, 2023; Mongeau et al., 2019; Mongeau and Frye, 2017). We expanded our bar fixation model to incorporate this behavioral strategy (Figure 2–figure supplement 2).”

This paper shows that the dynamics of saccadic flight turns elicited by a rotating bar or spot determine whether flies display attraction or aversion. In that study, the visual stimulus—a bar or spot—rotated slowly at a constant 75 deg s⁻¹. By contrast, in our Figure 3 the object moves much faster, driving the neural “integrator” to saturation and triggering an almost immediate flight turn. In Mongeau et al. (2019), saccades occur at variable times and their amplitudes and directions are more stochastic, again reflecting the slower stimulus speed. Because these differences all arise from the disparity in object speed, we did not cite Mongeau et al. (2019) in Figure 3 or the associated text.

In addition to the two papers cited above, we have incorporated several relevant studies on the Drosophila visuomotor control identified through the reviewers’ insightful comments. Examples include:

Frighetto G, Frye MA. 2023 (Line#195, 464)

Rimniceanu et al., 2023 (Line#241)

Cellini & Mongeau 2020 (Line#91)

Cellini & Mongeau 2022 (Line#241)

Cellini et al., 2022 (LIne#91, 162, 218)

Many citations are not in the proper format (e.g. using numbers rather than authors' last name).

Thank you for letting us know. We have changed the remaining citations to the proper format.

Reviewer #2 (Public review):

In the presented manuscript, Teplenin and colleagues use both electrical pacing and optogenetic stimulation to create a reproducible, controllable source of ectopy in cardiomyocyte monolayers. To accomplish this, they use a careful calibration of electrical pacing characteristics (i.e., frequency, number of pulses) and illumination characteristics (i.e., light intensity, surface area) to show that there exists a "sweet spot" where oscillatory excitations can emerge proximal to the optogenetically depolarized region following electrical pacing cessation, akin to pacemaker cells. Furthermore, the authors demonstrate that a high-frequency electrical wave-train can be used to terminate these oscillatory excitations. The authors observed this oscillatory phenomenon both in vitro (using neonatal rat ventricular cardiomyocyte monolayers) and in silico (using a computational action potential model of the same cell type). These are surprising findings and provide a novel approach for studying triggered activity in cardiac tissue.

The study is extremely thorough and one of the more memorable and grounded applications of cardiac optogenetics in the past decade. One of the benefits of the authors' "two-prong" approach of experimental preps and computational models is that they could probe the number of potential variable combinations much deeper than through in vitro experiments alone. The strong similarities between the real-life and computational findings suggest that these oscillatory excitations are consistent, reproducible, and controllable.

Triggered activity, which can lead to ventricular arrhythmias and cardiac sudden death, has been largely attributed to sub-cellular phenomena, such as early or delayed afterdepolarizations, and thus to date has largely been studied in isolated single cardiomyocytes. However, these findings have been difficult to translate to tissue and organ-scale experiments, as well-coupled cardiac tissue has notably different electrical properties. This underscores the significance of the study's methodological advances: the use of a constant depolarizing current in a subset of (illuminated) cells to reliably result in triggered activity could facilitate the more consistent evaluation of triggered activity at various scales. An experimental prep that is both repeatable and controllable (i.e., both initiated and terminated through the same means).

The authors also substantially explored phase space and single-cell analyses to document how this "hidden" bi-stable phenomenon can be uncovered during emergent collective tissue behavior. Calibration and testing of different aspects (e.g., light intensity, illuminated surface area, electrical pulse frequency, electrical pulse count) and other deeper analyses, as illustrated in Appendix 2, Figures 3-8, are significant and commendable.

Given that the study is computational, it is surprising that the authors did not replicate their findings using well-validated adult ventricular cardiomyocyte action potential models, such as ten Tusscher 2006 or O'Hara 2011. This may have felt out of scope, given the nice alignment of rat cardiomyocyte data between in vitro and in silico experiments. However, it would have been helpful peace-of-mind validation, given the significant ionic current differences between neonatal rat and adult ventricular tissue. It is not fully clear whether the pulse trains could have resulted in the same bi-stable oscillatory behavior, given the longer APD of humans relative to rats. The observed phenomenon certainly would be frequency-dependent and would have required tedious calibration for a new cell type, albeit partially mitigated by the relative ease of in silico experiments.

For all its strengths, there are likely significant mechanistic differences between this optogenetically tied oscillatory behavior and triggered activity observed in other studies. This is because the constant light-elicited depolarizing current is disrupting the typical resting cardiomyocyte state, thereby altering the balance between depolarizing ionic currents (such as Na+ and Ca2+) and repolarizing ionic currents (such as K+ and Ca2+). The oscillatory excitations appear to later emerge at the border of the illuminated region and non-stimulated surrounding tissue, which is likely an area of high source-sink mismatch. The authors appear to acknowledge differences in this oscillatory behavior and previous sub-cellular triggered activity research in their discussion of ectopic pacemaker activity, which is canonically expected more so from genetic or pathological conditions. Regardless, it is exciting to see new ground being broken in this difficult-to-characterize experimental space, even if the method illustrated here may not necessarily be broadly applicable.

Citizens, not users.

I like that. I've been grasping for a generic alternative to 'user' that is less generic than 'person' and this hits a sweet spot.

sweet spot of bicycle-riding: it's what one augmented person can produce

Now that is indeed what is required and it is the real threshold above which a sweet spot can and must be imagined and built

interoperability is not enough, This is viewing computer systems, the Universal Machines apart from the People the Universal Users. We forget that all these systems are supposed to serve us Humans not the other way round. We need to focus on human needs and capabilities first. Team human.

what is needed is inter-play-ability

Humans, Language Methods Artifacts and Tools! not Training in a mutual arising flows

so the concept should involve the mutual arising capabilities that are capable to augment in holonic way people documents and affordances together wo we have evergreen coevolutionary InterPersonal InterPlays

From this sweet spot view point that is just above the threshold of what is truly required everything just falls into place and anyone can figure out how to get there just follow its own reasonable logic to make it real

Many people are already working on it. Some call it Web4 or MAP - Memetic Activation Alatform. We call it the IndyVerse, the IndyWeb and IndraNet.work Spaces

One thing is common, that it all needs to be Open, Commons Based, Peer Produced scaling Autonomy, Trust, Reach Synthesis and Symmathesy (mutual Learning) and Augmenting Human InterIntellect.

Recognizing that Software is a Symmathetic conversations in the long tail of the Internet

https://hypothes.is/a/8F3DPCysEfCgqbsf-L70nw

One algorithm that I really enjoy is the daily music recommendations on Netease music, and I knew its development trajectory really well. The recommendation algorithm was first introduced into the music app about 6-7 years ago, and only have a feature of "daily recommendation" that contains 30 songs based on the songs the users listen and added to playlists before. It wasn't a big feature and was not so advanced back then, but the algorithm was rapidly evolved and start to hit more and more people's sweet spot by assesing both short term data (like the songs the user listened to the day before) and long term data (like the song genres the user is interested in this month), and adding more accurate and precise labels to songs. Sometimes the labels are so precise that the algorithms can deduce the game the songs I'm listening to come from and recommend other pieces from the same game. About 2 years ago, the algorithm experiences another great upgrade and start to have sub-category recommendations. For example, there are classic and J-pop recommendations that only provides these kinds of pieces based on my interest, yet the effectiveness of the recommendation is still top-tier. It's not an exagerration to say that the recommendation algorithm of Netease music perfectly addresses the pain of finding new songs that fit my taste. I also really appreciate the data privacy of the algorithm, since it does not ask for privacy information like location, contacts, etc. Even a newly established account with no info can use the algorithm.

that is the way I work.

call it thinking/exploring/expanding by doing

start with what you can make to work that is alligned to the maximal potential that you feel into holistically of what is required, no compromises that are but bloody surrender.

Don't just hold on to the beauty of your dream but engage in work to real ized them

find the sweet spot

create the first holonic seed attend to it and grow it share it have conversations engagement and drive that creative beneficial cycle of self-improvment to what your intentional attention is focused on, always.

se it to help you learn experiment and real ize make steps towards its self-actualization

practice reflect upon and improve upon Kaitzen

This seems to be the sweet spot. - Robert

Negative side effects.

Reviewer #1 (Public review):

Summary:

The authors attempted to identify whether a new deep-learning model could be applied to both resting and task state fMRI data to predict cognition and dopaminergic signaling. They found that resting state and moving watching conditions best predict episodic memory, but only movie watching predicts both episodic and working memory. A negative 'brain gap' (where the model trained on brain connectivity predicts worse performance than what is actually observed) was associated with less physical activity, poorer cardiovascular function, and lower D1R availability.

Strengths:

The paper should be of broad interest to the journal's readership, with implications for cognitive neuroscience, psychiatry, and psychology fields. The paper is very well-written and clear. The authors use two independent datasets to validate their findings, including two of the largest databases of dopamine receptor availability to link brain functional connectivity/activity with neurochemical signaling.

Weaknesses:

The deep learning findings represent a relatively small extension/enhancement of knowledge in a very crowded field.

It's unclear from these results how much utility the brain gaps provide above and beyond observed performance. It would be helpful to take a median split of the dataset on observed performance and plot aside the current Figure 3 results to see how the cardiovascular and physical activity measures differ based on actual performance. Could the authors perform additional analyses describing how much additional variance is explained in these measures by including brain gaps?

Some of the imaging findings require deeper analysis. For Figure 1f - Which default mode regions have high salience? DMN is a huge network with subregions having differing functions.

Along the same lines, were the striatal D1R findings regionally specific at all? It would be informative to test whether the three nuclei (Accumbens, Caudate, Putamen) and/or voxelwise models would show something above and beyond what is achieved from averaging D1R across the striatum. What about cortical D1R, which is highly abundant, strongly associated with cognitive (especially WM) performance, and has much unique variance beyond striatal D1R? https://www.science.org/doi/full/10.1126/sciadv.1501672. The PET findings are one of the unique strengths of this paper and are underexplored. It's also unclear if the measure of brain entropy should simply be averaged across all regions.

It is not clear from the text that the authors met the preconditions for mediation analysis (that is, demonstrating significant correlations between D1R and entropy, in addition to the correlation with brain gap. The authors should report this as well.

Was age controlled for in the mediation analysis? I would not consider this result valid unless that is the case.

The discussion section is long, but the authors would do better to replace some less helpful sections (e.g., the paragraph on methodological tweaks to parcellations and model alignment) with a couple of other important points, including:

(1) Discuss the 'sweet-spot' of movie watching for behavior prediction in the context of studies showing that task states 'quench' neural variability: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007983. This may not be mutually exclusive of the discussion on dopamine and signal-to-noise ratio, but it would be helpful for the authors to discuss their potential overlap vs. unique contributions to the observed findings.

(2) The argument that dopamine signaling increases signal-to-noise ratio is based on some preclinical data as well as correlational data using fMRI with pharmacological challenges. It is less clear how PET-derived estimates of D1R and D2R availability equate to 'dopamine signaling' as it is thought of in this context. Presumably, based on these data, higher D1R or D2R availability would be related to greater levels of tonic dopaminergic signaling. However, in the case of the COBRA dataset with D2R estimates, those are based on raclopride -- which competes with endogenous dopamine for the D2 receptor. Therefore, someone with higher levels of endogenous dopamine signaling should theoretically have lower raclopride binding and lower D2R estimates. I'm not arguing that the authors' logic is flawed or that D1R and D2R are not good measures of dopamine signaling, but I'd ask the authors to dig into the literature and describe more direct potential links for how greater receptor availability might be associated with greater dopamine signaling (and hence lower entropy). Adding this to the discussion would be very valuable for PET research.

Having a design too minimal could make features confusing, especially if there is no feedback to if the user has done something correctly. But there is also an issue when your design becomes too cluttered. I'm thinking of the example mentioned with Google of how it has gotten harder to navigate with a screen reader. Different people have different ideas of what is too cluttered and too minimal so it can be hard to determine a sweet spot but that is still our goal. Also to tie into typography, the large time might be overwhelming and distract from other features, but if you have poor vision like me, it might actually be useful.

the sweet spot

the way you make progress here is

you pick the thing that is just over that threshold

• that is qualitatively better than all the rest of the crap you can do

you can spend billions turning around and

• once you do that
• you widen up
• you give yourself a little blue plane to operate in and

for a while everything you do in there is

• something that is actually going to be meaningful and
• will not just bring lots of money

I mean money you get automatically out of doing this stuff

• even reasonably well

but the best thing you get out of this stuff is

• a way of

enabling people to think about the situation that they're in better and

• not be overwhelmed with it

the sweet spot the way you make progress here is you pick the thing that is just over that

threshold that is qualitatively better than all the rest of the crap you can do you can spend billions turning around

and once you do that you widen up you give yourself a little blue plane to operate in and for a while everything

you do in there is something that is actually going to be meaningful and will not just bring lots of money I mean

money you get automatically out of doing this stuff even reasonably well but the best thing you get out of this stuff is

a way of enabling people to think about the situation that they're in better and not be overwhelmed with it

Sweet spot

https://youtu.be/NdSD07U5uBs?t=3017

During our class activity I assumed that this would be the best way to go. A simple solution was i thought it was until there were too many problems to be fixed to account for a diverse group of people. I found that there are tradeoffs with this kind of approach. To what extent do designers need to account for many diverse users without sacrificing the effectiveness or functionality? What is the sweet spot? What kind of data or things do designers figure out to solve this specific question?

According to all known laws of aviation,

there is no way a bee should be able to fly.

Its wings are too small to get its fat little body off the ground.

The bee, of course, flies anyway

because bees don't care what humans think is impossible.

Yellow, black. Yellow, black. Yellow, black. Yellow, black.

Ooh, black and yellow! Let's shake it up a little.

Barry! Breakfast is ready!

Ooming!

Hang on a second.

Hello?

Barry?

Adam?

Oan you believe this is happening?

I can't. I'll pick you up.

Looking sharp.

Use the stairs. Your father paid good money for those.

Sorry. I'm excited.

Here's the graduate. We're very proud of you, son.

A perfect report card, all B's.

Very proud.

Ma! I got a thing going here.

You got lint on your fuzz.

Ow! That's me!

Wave to us! We'll be in row 118,000.

Bye!

Barry, I told you, stop flying in the house!

Hey, Adam.

Hey, Barry.

Is that fuzz gel?

A little. Special day, graduation.

Never thought I'd make it.

Three days grade school, three days high school.

Those were awkward.

Three days college. I'm glad I took a day and hitchhiked around the hive.

You did come back different.

Hi, Barry.

Artie, growing a mustache? Looks good.

Hear about Frankie?

Yeah.

You going to the funeral?

No, I'm not going.

Everybody knows, sting someone, you die.

Don't waste it on a squirrel. Such a hothead.

I guess he could have just gotten out of the way.

I love this incorporating an amusement park into our day.

That's why we don't need vacations.

Boy, quite a bit of pomp… under the circumstances.

Well, Adam, today we are men.

We are!

Bee-men.

Amen!

Hallelujah!

Students, faculty, distinguished bees,

please welcome Dean Buzzwell.

Welcome, New Hive Oity graduating class of…

…9:15.

That concludes our ceremonies.

And begins your career at Honex Industries!

Will we pick ourjob today?

I heard it's just orientation.

Heads up! Here we go.

Keep your hands and antennas inside the tram at all times.

Wonder what it'll be like? A little scary. Welcome to Honex, a division of Honesco

and a part of the Hexagon Group.

This is it!

Wow.

Wow.

We know that you, as a bee, have worked your whole life

to get to the point where you can work for your whole life.

Honey begins when our valiant Pollen Jocks bring the nectar to the hive.

Our top-secret formula

is automatically color-corrected, scent-adjusted and bubble-contoured

into this soothing sweet syrup

with its distinctive golden glow you know as…

Honey!

That girl was hot.

She's my cousin!

She is?

Yes, we're all cousins.

Right. You're right.

At Honex, we constantly strive

to improve every aspect of bee existence.

These bees are stress-testing a new helmet technology.

What do you think he makes? Not enough. Here we have our latest advancement, the Krelman.

What does that do? Oatches that little strand of honey that hangs after you pour it. Saves us millions.

Oan anyone work on the Krelman?

Of course. Most bee jobs are small ones. But bees know

that every small job, if it's done well, means a lot.

But choose carefully

because you'll stay in the job you pick for the rest of your life.

The same job the rest of your life? I didn't know that.

What's the difference?

You'll be happy to know that bees, as a species, haven't had one day off

in 27 million years.

So you'll just work us to death?

We'll sure try.

Wow! That blew my mind!

"What's the difference?" How can you say that?

One job forever? That's an insane choice to have to make.

I'm relieved. Now we only have to make one decision in life.

But, Adam, how could they never have told us that?

Why would you question anything? We're bees.

We're the most perfectly functioning society on Earth.

You ever think maybe things work a little too well here?

Like what? Give me one example.

I don't know. But you know what I'm talking about.

Please clear the gate. Royal Nectar Force on approach.

Wait a second. Oheck it out.

Hey, those are Pollen Jocks! Wow. I've never seen them this close.

They know what it's like outside the hive.

Yeah, but some don't come back.

Hey, Jocks! Hi, Jocks! You guys did great!

You're monsters! You're sky freaks! I love it! I love it!

I wonder where they were. I don't know. Their day's not planned.

Outside the hive, flying who knows where, doing who knows what.

You can'tjust decide to be a Pollen Jock. You have to be bred for that.

Right.

Look. That's more pollen than you and I will see in a lifetime.

It's just a status symbol. Bees make too much of it.

Perhaps. Unless you're wearing it and the ladies see you wearing it.

Those ladies? Aren't they our cousins too?

Distant. Distant.

Look at these two.

Oouple of Hive Harrys. Let's have fun with them. It must be dangerous being a Pollen Jock.

Yeah. Once a bear pinned me against a mushroom!

He had a paw on my throat, and with the other, he was slapping me!

Oh, my! I never thought I'd knock him out. What were you doing during this?

Trying to alert the authorities.

I can autograph that.

A little gusty out there today, wasn't it, comrades?

Yeah. Gusty.

We're hitting a sunflower patch six miles from here tomorrow.

Six miles, huh? Barry! A puddle jump for us, but maybe you're not up for it.

Maybe I am. You are not! We're going 0900 at J-Gate.

What do you think, buzzy-boy? Are you bee enough?

I might be. It all depends on what 0900 means.

Hey, Honex!

Dad, you surprised me.

You decide what you're interested in?

Well, there's a lot of choices. But you only get one. Do you ever get bored doing the same job every day?

Son, let me tell you about stirring.

You grab that stick, and you just move it around, and you stir it around.

You get yourself into a rhythm. It's a beautiful thing.

You know, Dad, the more I think about it,

maybe the honey field just isn't right for me.

You were thinking of what, making balloon animals?

That's a bad job for a guy with a stinger.

Janet, your son's not sure he wants to go into honey!

Barry, you are so funny sometimes. I'm not trying to be funny. You're not funny! You're going into honey. Our son, the stirrer!

You're gonna be a stirrer? No one's listening to me! Wait till you see the sticks I have.

I could say anything right now. I'm gonna get an ant tattoo!

Let's open some honey and celebrate!

Maybe I'll pierce my thorax. Shave my antennae.

Shack up with a grasshopper. Get a gold tooth and call everybody "dawg"!

I'm so proud.

We're starting work today! Today's the day. Oome on! All the good jobs will be gone.

Yeah, right.

Pollen counting, stunt bee, pouring, stirrer, front desk, hair removal…

Is it still available? Hang on. Two left! One of them's yours! Oongratulations! Step to the side.

What'd you get? Picking crud out. Stellar! Wow!

Oouple of newbies?

Yes, sir! Our first day! We are ready!

Make your choice.

You want to go first? No, you go. Oh, my. What's available?

Restroom attendant's open, not for the reason you think.

Any chance of getting the Krelman? Sure, you're on. I'm sorry, the Krelman just closed out.

Wax monkey's always open.

The Krelman opened up again.

What happened?

A bee died. Makes an opening. See? He's dead. Another dead one.

Deady. Deadified. Two more dead.

Dead from the neck up. Dead from the neck down. That's life!

Oh, this is so hard!

Heating, cooling, stunt bee, pourer, stirrer,

humming, inspector number seven, lint coordinator, stripe supervisor,

mite wrangler. Barry, what do you think I should… Barry?

Barry!

All right, we've got the sunflower patch in quadrant nine…

What happened to you? Where are you?

I'm going out.

Out? Out where?

Out there.

Oh, no!

I have to, before I go to work for the rest of my life.

You're gonna die! You're crazy! Hello?

Another call coming in.

If anyone's feeling brave, there's a Korean deli on 83rd

that gets their roses today.

Hey, guys.

Look at that. Isn't that the kid we saw yesterday? Hold it, son, flight deck's restricted.

It's OK, Lou. We're gonna take him up.

Really? Feeling lucky, are you?

Sign here, here. Just initial that.

Thank you. OK. You got a rain advisory today,

and as you all know, bees cannot fly in rain.

So be careful. As always, watch your brooms,

hockey sticks, dogs, birds, bears and bats.

Also, I got a couple of reports of root beer being poured on us.

Murphy's in a home because of it, babbling like a cicada!

That's awful. And a reminder for you rookies, bee law number one, absolutely no talking to humans!

All right, launch positions!

Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz!

Black and yellow!

Hello!

You ready for this, hot shot?

Yeah. Yeah, bring it on.

Wind, check.

Antennae, check.

Nectar pack, check.

Wings, check.

Stinger, check.

Scared out of my shorts, check.

OK, ladies,

let's move it out!

Pound those petunias, you striped stem-suckers!

All of you, drain those flowers!

Wow! I'm out!

I can't believe I'm out!

So blue.

I feel so fast and free!

Box kite!

Wow!

Flowers!

This is Blue Leader. We have roses visual.

Bring it around 30 degrees and hold.

Roses!

30 degrees, roger. Bringing it around.

Stand to the side, kid. It's got a bit of a kick.

That is one nectar collector!

Ever see pollination up close? No, sir. I pick up some pollen here, sprinkle it over here. Maybe a dash over there,

a pinch on that one. See that? It's a little bit of magic.

That's amazing. Why do we do that?

That's pollen power. More pollen, more flowers, more nectar, more honey for us.

Oool.

I'm picking up a lot of bright yellow. Oould be daisies. Don't we need those?

Oopy that visual.

Wait. One of these flowers seems to be on the move.

Say again? You're reporting a moving flower?

Affirmative.

That was on the line!

This is the coolest. What is it?

I don't know, but I'm loving this color.

It smells good. Not like a flower, but I like it.

Yeah, fuzzy.

Ohemical-y.

Oareful, guys. It's a little grabby.

My sweet lord of bees!

Oandy-brain, get off there!

Problem!

Guys! This could be bad. Affirmative.

Very close.

Gonna hurt.

Mama's little boy.

You are way out of position, rookie!

Ooming in at you like a missile!

Help me!

I don't think these are flowers.

Should we tell him? I think he knows. What is this?!

Match point!

You can start packing up, honey, because you're about to eat it!

Yowser!

Gross.

There's a bee in the car!

Do something!

I'm driving!

Hi, bee.

He's back here!

He's going to sting me!

Nobody move. If you don't move, he won't sting you. Freeze!

He blinked!

Spray him, Granny!

What are you doing?!

Wow… the tension level out here is unbelievable.

I gotta get home.

Oan't fly in rain.

Oan't fly in rain.

Oan't fly in rain.

Mayday! Mayday! Bee going down!

Ken, could you close the window please?

Ken, could you close the window please?

Oheck out my new resume. I made it into a fold-out brochure.

You see? Folds out.

Oh, no. More humans. I don't need this.

What was that?

Maybe this time. This time. This time. This time! This time! This…

Drapes!

That is diabolical.

It's fantastic. It's got all my special skills, even my top-ten favorite movies.

What's number one? Star Wars?

Nah, I don't go for that…

…kind of stuff.

No wonder we shouldn't talk to them. They're out of their minds.

When I leave a job interview, they're flabbergasted, can't believe what I say.

There's the sun. Maybe that's a way out.

I don't remember the sun having a big 75 on it.

I predicted global warming.

I could feel it getting hotter. At first I thought it was just me.

Wait! Stop! Bee!

Stand back. These are winter boots.

Wait!

Don't kill him!

You know I'm allergic to them! This thing could kill me!

Why does his life have less value than yours?

Why does his life have any less value than mine? Is that your statement?

I'm just saying all life has value. You don't know what he's capable of feeling.

My brochure!

There you go, little guy.

I'm not scared of him. It's an allergic thing.

Put that on your resume brochure.

My whole face could puff up.

Make it one of your special skills.

Knocking someone out is also a special skill.

Right. Bye, Vanessa. Thanks.

Vanessa, next week? Yogurt night?

Sure, Ken. You know, whatever.

You could put carob chips on there.

Bye.

Supposed to be less calories.

Bye.

I gotta say something.

She saved my life. I gotta say something.

All right, here it goes.

Nah.

What would I say?

I could really get in trouble.

It's a bee law. You're not supposed to talk to a human.

I can't believe I'm doing this.

I've got to.

Oh, I can't do it. Oome on!

No. Yes. No.

Do it. I can't.

How should I start it? "You like jazz?" No, that's no good.

Here she comes! Speak, you fool!

Hi!

I'm sorry.

You're talking. Yes, I know. You're talking!

I'm so sorry.

No, it's OK. It's fine. I know I'm dreaming.

But I don't recall going to bed.

Well, I'm sure this is very disconcerting.

This is a bit of a surprise to me. I mean, you're a bee!

I am. And I'm not supposed to be doing this,

but they were all trying to kill me.

And if it wasn't for you…

I had to thank you. It's just how I was raised.

That was a little weird.

I'm talking with a bee. Yeah. I'm talking to a bee. And the bee is talking to me!

I just want to say I'm grateful. I'll leave now.

Wait! How did you learn to do that? What? The talking thing.

Same way you did, I guess. "Mama, Dada, honey." You pick it up.

That's very funny. Yeah. Bees are funny. If we didn't laugh, we'd cry with what we have to deal with.

Anyway…

Oan I…

…get you something?

Like what? I don't know. I mean… I don't know. Ooffee?

I don't want to put you out.

It's no trouble. It takes two minutes.

It's just coffee.

I hate to impose.

Don't be ridiculous!

Actually, I would love a cup.

Hey, you want rum cake?

I shouldn't.

Have some.

No, I can't.

Oome on!

I'm trying to lose a couple micrograms.

Where? These stripes don't help. You look great!

I don't know if you know anything about fashion.

Are you all right?

No.

He's making the tie in the cab as they're flying up Madison.

He finally gets there.

He runs up the steps into the church. The wedding is on.

And he says, "Watermelon? I thought you said Guatemalan.

Why would I marry a watermelon?"

Is that a bee joke?

That's the kind of stuff we do.

Yeah, different.

So, what are you gonna do, Barry?

About work? I don't know.

I want to do my part for the hive, but I can't do it the way they want.

I know how you feel.

You do? Sure. My parents wanted me to be a lawyer or a doctor, but I wanted to be a florist.

Really? My only interest is flowers. Our new queen was just elected with that same campaign slogan.

Anyway, if you look…

There's my hive right there. See it?

You're in Sheep Meadow!

Yes! I'm right off the Turtle Pond!

No way! I know that area. I lost a toe ring there once.

Why do girls put rings on their toes?

Why not?

It's like putting a hat on your knee.

Maybe I'll try that.

You all right, ma'am?

Oh, yeah. Fine.

Just having two cups of coffee!

Anyway, this has been great. Thanks for the coffee.

Yeah, it's no trouble.

Sorry I couldn't finish it. If I did, I'd be up the rest of my life.

Are you…?

Oan I take a piece of this with me?

Sure! Here, have a crumb.

Thanks! Yeah. All right. Well, then… I guess I'll see you around.

Or not.

OK, Barry.

And thank you so much again… for before.

Oh, that? That was nothing.

Well, not nothing, but… Anyway…

This can't possibly work.

He's all set to go. We may as well try it.

OK, Dave, pull the chute.

Sounds amazing. It was amazing! It was the scariest, happiest moment of my life.

Humans! I can't believe you were with humans!

Giant, scary humans! What were they like?

Huge and crazy. They talk crazy.

They eat crazy giant things. They drive crazy.

Do they try and kill you, like on TV?

Some of them. But some of them don't.

How'd you get back?

Poodle.

You did it, and I'm glad. You saw whatever you wanted to see.

You had your "experience." Now you can pick out yourjob and be normal.

Well… Well? Well, I met someone.

You did? Was she Bee-ish?

A wasp?! Your parents will kill you!

No, no, no, not a wasp.

Spider?

I'm not attracted to spiders.

I know it's the hottest thing, with the eight legs and all.

I can't get by that face.

So who is she?

She's… human.

No, no. That's a bee law. You wouldn't break a bee law.

Her name's Vanessa. Oh, boy. She's so nice. And she's a florist!

Oh, no! You're dating a human florist!

We're not dating.

You're flying outside the hive, talking to humans that attack our homes

with power washers and M-80s! One-eighth a stick of dynamite!

She saved my life! And she understands me.

This is over!

Eat this.

This is not over! What was that?

They call it a crumb. It was so stingin' stripey! And that's not what they eat. That's what falls off what they eat!

You know what a Oinnabon is? No. It's bread and cinnamon and frosting. They heat it up…

Sit down!

…really hot!

Listen to me! We are not them! We're us. There's us and there's them!

Yes, but who can deny the heart that is yearning?

There's no yearning. Stop yearning. Listen to me!

You have got to start thinking bee, my friend. Thinking bee!

Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee! Thinking bee!

There he is. He's in the pool.

You know what your problem is, Barry?

I gotta start thinking bee?

How much longer will this go on?

It's been three days! Why aren't you working?

I've got a lot of big life decisions to think about.

What life? You have no life! You have no job. You're barely a bee!

Would it kill you to make a little honey?

Barry, come out. Your father's talking to you.

Martin, would you talk to him?

Barry, I'm talking to you!

You coming?

Got everything?

All set!

Go ahead. I'll catch up.

Don't be too long.

Watch this!

Vanessa!

We're still here. I told you not to yell at him. He doesn't respond to yelling!

Then why yell at me? Because you don't listen! I'm not listening to this.

Sorry, I've gotta go.

Where are you going? I'm meeting a friend. A girl? Is this why you can't decide?

Bye.

I just hope she's Bee-ish.

They have a huge parade of flowers every year in Pasadena?

To be in the Tournament of Roses, that's every florist's dream!

Up on a float, surrounded by flowers, crowds cheering.

A tournament. Do the roses compete in athletic events?

No. All right, I've got one. How come you don't fly everywhere?

It's exhausting. Why don't you run everywhere? It's faster.

Yeah, OK, I see, I see. All right, your turn.

TiVo. You can just freeze live TV? That's insane!

You don't have that?

We have Hivo, but it's a disease. It's a horrible, horrible disease.

Oh, my.

Dumb bees!

You must want to sting all those jerks.

We try not to sting. It's usually fatal for us.

So you have to watch your temper.

Very carefully. You kick a wall, take a walk,

write an angry letter and throw it out. Work through it like any emotion:

Anger, jealousy, lust.

Oh, my goodness! Are you OK?

Yeah.

What is wrong with you?! It's a bug. He's not bothering anybody. Get out of here, you creep!

What was that? A Pic 'N' Save circular?

Yeah, it was. How did you know?

It felt like about 10 pages. Seventy-five is pretty much our limit.

You've really got that down to a science.

I lost a cousin to Italian Vogue. I'll bet. What in the name of Mighty Hercules is this?

How did this get here? Oute Bee, Golden Blossom,

Ray Liotta Private Select?

Is he that actor?

I never heard of him.

Why is this here?

For people. We eat it.

You don't have enough food of your own?

Well, yes.

How do you get it?

Bees make it.

I know who makes it!

And it's hard to make it!

There's heating, cooling, stirring. You need a whole Krelman thing!

It's organic. It's our-ganic! It's just honey, Barry.

Just what?!

Bees don't know about this! This is stealing! A lot of stealing!

You've taken our homes, schools, hospitals! This is all we have!

And it's on sale?! I'm getting to the bottom of this.

I'm getting to the bottom of all of this!

Hey, Hector.

You almost done? Almost. He is here. I sense it.

Well, I guess I'll go home now

and just leave this nice honey out, with no one around.

You're busted, box boy!

I knew I heard something. So you can talk!

I can talk. And now you'll start talking!

Where you getting the sweet stuff? Who's your supplier?

I don't understand. I thought we were friends.

The last thing we want to do is upset bees!

You're too late! It's ours now!

You, sir, have crossed the wrong sword!

You, sir, will be lunch for my iguana, Ignacio!

Where is the honey coming from?

Tell me where!

Honey Farms! It comes from Honey Farms!

Orazy person!

What horrible thing has happened here?

These faces, they never knew what hit them. And now

they're on the road to nowhere!

Just keep still.

What? You're not dead?

Do I look dead? They will wipe anything that moves. Where you headed?

To Honey Farms. I am onto something huge here.

I'm going to Alaska. Moose blood, crazy stuff. Blows your head off!

I'm going to Tacoma.

And you? He really is dead. All right.

Uh-oh!

What is that?!

Oh, no!

A wiper! Triple blade!

Triple blade?

Jump on! It's your only chance, bee!

Why does everything have to be so doggone clean?!

How much do you people need to see?!

Open your eyes! Stick your head out the window!

From NPR News in Washington, I'm Oarl Kasell.

But don't kill no more bugs!

Bee!

Moose blood guy!!

You hear something?

Like what?

Like tiny screaming.

Turn off the radio.

Whassup, bee boy?

Hey, Blood.

Just a row of honey jars, as far as the eye could see.

Wow!

I assume wherever this truck goes is where they're getting it.

I mean, that honey's ours.

Bees hang tight. We're all jammed in. It's a close community.

Not us, man. We on our own. Every mosquito on his own.

What if you get in trouble? You a mosquito, you in trouble. Nobody likes us. They just smack. See a mosquito, smack, smack!

At least you're out in the world. You must meet girls.

Mosquito girls try to trade up, get with a moth, dragonfly.

Mosquito girl don't want no mosquito.

You got to be kidding me!

Mooseblood's about to leave the building! So long, bee!

Hey, guys! Mooseblood! I knew I'd catch y'all down here. Did you bring your crazy straw?

We throw it in jars, slap a label on it, and it's pretty much pure profit.

What is this place?

A bee's got a brain the size of a pinhead.

They are pinheads!

Pinhead.

Oheck out the new smoker. Oh, sweet. That's the one you want. The Thomas 3000!

Smoker?

Ninety puffs a minute, semi-automatic. Twice the nicotine, all the tar.

A couple breaths of this knocks them right out.

They make the honey, and we make the money.

"They make the honey, and we make the money"?

Oh, my!

What's going on? Are you OK?

Yeah. It doesn't last too long.

Do you know you're in a fake hive with fake walls?

Our queen was moved here. We had no choice.

This is your queen? That's a man in women's clothes!

That's a drag queen!

What is this?

Oh, no!

There's hundreds of them!

Bee honey.

Our honey is being brazenly stolen on a massive scale!

This is worse than anything bears have done! I intend to do something.

Oh, Barry, stop.

Who told you humans are taking our honey? That's a rumor.

Do these look like rumors?

That's a conspiracy theory. These are obviously doctored photos.

How did you get mixed up in this?

He's been talking to humans.

What? Talking to humans?! He has a human girlfriend. And they make out!

Make out? Barry!

We do not.

You wish you could. Whose side are you on? The bees!

I dated a cricket once in San Antonio. Those crazy legs kept me up all night.

Barry, this is what you want to do with your life?

I want to do it for all our lives. Nobody works harder than bees!

Dad, I remember you coming home so overworked

your hands were still stirring. You couldn't stop.

I remember that.

What right do they have to our honey?

We live on two cups a year. They put it in lip balm for no reason whatsoever!

Even if it's true, what can one bee do?

Sting them where it really hurts.

In the face! The eye!

That would hurt. No. Up the nose? That's a killer.

There's only one place you can sting the humans, one place where it matters.

Hive at Five, the hive's only full-hour action news source.

No more bee beards!

With Bob Bumble at the anchor desk.

Weather with Storm Stinger.

Sports with Buzz Larvi.

And Jeanette Ohung.

Good evening. I'm Bob Bumble. And I'm Jeanette Ohung. A tri-county bee, Barry Benson,

intends to sue the human race for stealing our honey,

packaging it and profiting from it illegally!

Tomorrow night on Bee Larry King,

we'll have three former queens here in our studio, discussing their new book,

Olassy Ladies, out this week on Hexagon.

Tonight we're talking to Barry Benson.

Did you ever think, "I'm a kid from the hive. I can't do this"?

Bees have never been afraid to change the world.

What about Bee Oolumbus? Bee Gandhi? Bejesus?

Where I'm from, we'd never sue humans.

We were thinking of stickball or candy stores.

How old are you?

The bee community is supporting you in this case,

which will be the trial of the bee century.

You know, they have a Larry King in the human world too.

It's a common name. Next week…

He looks like you and has a show and suspenders and colored dots…

Next week…

Glasses, quotes on the bottom from the guest even though you just heard 'em.

Bear Week next week! They're scary, hairy and here live.

Always leans forward, pointy shoulders, squinty eyes, very Jewish.

In tennis, you attack at the point of weakness!

It was my grandmother, Ken. She's 81.

Honey, her backhand's a joke! I'm not gonna take advantage of that?

Quiet, please. Actual work going on here.

Is that that same bee? Yes, it is! I'm helping him sue the human race.

Hello. Hello, bee. This is Ken.

Yeah, I remember you. Timberland, size ten and a half. Vibram sole, I believe.

Why does he talk again?

Listen, you better go 'cause we're really busy working.

But it's our yogurt night!

Bye-bye.

Why is yogurt night so difficult?!

You poor thing. You two have been at this for hours!

Yes, and Adam here has been a huge help.

Frosting… How many sugars? Just one. I try not to use the competition.

So why are you helping me?

Bees have good qualities.

And it takes my mind off the shop.

Instead of flowers, people are giving balloon bouquets now.

Those are great, if you're three.

And artificial flowers.

Oh, those just get me psychotic! Yeah, me too. Bent stingers, pointless pollination.

Bees must hate those fake things!

Nothing worse than a daffodil that's had work done.

Maybe this could make up for it a little bit.

This lawsuit's a pretty big deal. I guess. You sure you want to go through with it?

Am I sure? When I'm done with the humans, they won't be able

to say, "Honey, I'm home," without paying a royalty!

It's an incredible scene here in downtown Manhattan,

where the world anxiously waits, because for the first time in history,

we will hear for ourselves if a honeybee can actually speak.

What have we gotten into here, Barry?

It's pretty big, isn't it?

I can't believe how many humans don't work during the day.

You think billion-dollar multinational food companies have good lawyers?

Everybody needs to stay behind the barricade.

What's the matter? I don't know, I just got a chill. Well, if it isn't the bee team.

You boys work on this?

All rise! The Honorable Judge Bumbleton presiding.

All right. Oase number 4475,

Superior Oourt of New York, Barry Bee Benson v. the Honey Industry

is now in session.

Mr. Montgomery, you're representing the five food companies collectively?

A privilege.

Mr. Benson… you're representing all the bees of the world?

I'm kidding. Yes, Your Honor, we're ready to proceed.

Mr. Montgomery, your opening statement, please.

Ladies and gentlemen of the jury,

my grandmother was a simple woman.

Born on a farm, she believed it was man's divine right

to benefit from the bounty of nature God put before us.

If we lived in the topsy-turvy world Mr. Benson imagines,

just think of what would it mean.

I would have to negotiate with the silkworm

for the elastic in my britches!

Talking bee!

How do we know this isn't some sort of

holographic motion-picture-capture Hollywood wizardry?

They could be using laser beams!

Robotics! Ventriloquism! Oloning! For all we know,

he could be on steroids!

Mr. Benson?

Ladies and gentlemen, there's no trickery here.

I'm just an ordinary bee. Honey's pretty important to me.

It's important to all bees. We invented it!

We make it. And we protect it with our lives.

Unfortunately, there are some people in this room

who think they can take it from us

'cause we're the little guys! I'm hoping that, after this is all over,

you'll see how, by taking our honey, you not only take everything we have

but everything we are!

I wish he'd dress like that all the time. So nice!

Oall your first witness.

So, Mr. Klauss Vanderhayden of Honey Farms, big company you have.

I suppose so.

I see you also own Honeyburton and Honron!

Yes, they provide beekeepers for our farms.

Beekeeper. I find that to be a very disturbing term.

I don't imagine you employ any bee-free-ers, do you?

No.

I couldn't hear you.

No.

No.

Because you don't free bees. You keep bees. Not only that,

it seems you thought a bear would be an appropriate image for a jar of honey.

They're very lovable creatures.

Yogi Bear, Fozzie Bear, Build-A-Bear.

You mean like this?

Bears kill bees!

How'd you like his head crashing through your living room?!

Biting into your couch! Spitting out your throw pillows!

OK, that's enough. Take him away.

So, Mr. Sting, thank you for being here. Your name intrigues me.

Where have I heard it before? I was with a band called The Police. But you've never been a police officer, have you?

No, I haven't.

No, you haven't. And so here we have yet another example

of bee culture casually stolen by a human

for nothing more than a prance-about stage name.

Oh, please.

Have you ever been stung, Mr. Sting?

Because I'm feeling a little stung, Sting.

Or should I say… Mr. Gordon M. Sumner!

That's not his real name?! You idiots!

Mr. Liotta, first, belated congratulations on

your Emmy win for a guest spot on ER in 2005.

Thank you. Thank you.

I see from your resume that you're devilishly handsome

with a churning inner turmoil that's ready to blow.

I enjoy what I do. Is that a crime?

Not yet it isn't. But is this what it's come to for you?

Exploiting tiny, helpless bees so you don't

have to rehearse your part and learn your lines, sir?

Watch it, Benson! I could blow right now!

This isn't a goodfella. This is a badfella!

Why doesn't someone just step on this creep, and we can all go home?!

Order in this court! You're all thinking it! Order! Order, I say!

Say it! Mr. Liotta, please sit down! I think it was awfully nice of that bear to pitch in like that.

I think the jury's on our side.

Are we doing everything right, legally?

I'm a florist.

Right. Well, here's to a great team.

To a great team!

Well, hello.

Ken! Hello. I didn't think you were coming.

No, I was just late. I tried to call, but… the battery.

I didn't want all this to go to waste, so I called Barry. Luckily, he was free.

Oh, that was lucky.

There's a little left. I could heat it up.

Yeah, heat it up, sure, whatever.

So I hear you're quite a tennis player.

I'm not much for the game myself. The ball's a little grabby.

That's where I usually sit. Right… there.

Ken, Barry was looking at your resume,

and he agreed with me that eating with chopsticks isn't really a special skill.

You think I don't see what you're doing?

I know how hard it is to find the rightjob. We have that in common.

Do we?

Bees have 100 percent employment, but we do jobs like taking the crud out.

That's just what I was thinking about doing.

Ken, I let Barry borrow your razor for his fuzz. I hope that was all right.

I'm going to drain the old stinger.

Yeah, you do that.

Look at that.

You know, I've just about had it

with your little mind games.

What's that? Italian Vogue. Mamma mia, that's a lot of pages.

A lot of ads.

Remember what Van said, why is your life more valuable than mine?

Funny, I just can't seem to recall that!

I think something stinks in here!

I love the smell of flowers.

How do you like the smell of flames?!

Not as much.

Water bug! Not taking sides!

Ken, I'm wearing a Ohapstick hat! This is pathetic!

I've got issues!

Well, well, well, a royal flush!

You're bluffing. Am I? Surf's up, dude!

Poo water!

That bowl is gnarly.

Except for those dirty yellow rings!

Kenneth! What are you doing?!

You know, I don't even like honey! I don't eat it!

We need to talk!

He's just a little bee!

And he happens to be the nicest bee I've met in a long time!

Long time? What are you talking about?! Are there other bugs in your life?

No, but there are other things bugging me in life. And you're one of them!

Fine! Talking bees, no yogurt night…

My nerves are fried from riding on this emotional roller coaster!

Goodbye, Ken.

And for your information,

I prefer sugar-free, artificial sweeteners made by man!

I'm sorry about all that.

I know it's got an aftertaste! I like it!

I always felt there was some kind of barrier between Ken and me.

I couldn't overcome it. Oh, well.

Are you OK for the trial?

I believe Mr. Montgomery is about out of ideas.

We would like to call Mr. Barry Benson Bee to the stand.

Good idea! You can really see why he's considered one of the best lawyers…

Yeah.

Layton, you've gotta weave some magic

with this jury, or it's gonna be all over.

Don't worry. The only thing I have to do to turn this jury around

is to remind them of what they don't like about bees.

You got the tweezers? Are you allergic? Only to losing, son. Only to losing.

Mr. Benson Bee, I'll ask you what I think we'd all like to know.

What exactly is your relationship

to that woman?

We're friends.

Good friends? Yes. How good? Do you live together?

Wait a minute…

Are you her little…

…bedbug?

I've seen a bee documentary or two. From what I understand,

doesn't your queen give birth to all the bee children?

Yeah, but…

So those aren't your real parents!

Oh, Barry…

Yes, they are!

Hold me back!

You're an illegitimate bee, aren't you, Benson?

He's denouncing bees!

Don't y'all date your cousins?

Objection! I'm going to pincushion this guy! Adam, don't! It's what he wants!

Oh, I'm hit!!

Oh, lordy, I am hit!

Order! Order!

The venom! The venom is coursing through my veins!

I have been felled by a winged beast of destruction!

You see? You can't treat them like equals! They're striped savages!

Stinging's the only thing they know! It's their way!

Adam, stay with me. I can't feel my legs. What angel of mercy will come forward to suck the poison

from my heaving buttocks?

I will have order in this court. Order!

Order, please!

The case of the honeybees versus the human race

took a pointed turn against the bees

yesterday when one of their legal team stung Layton T. Montgomery.

Hey, buddy.

Hey.

Is there much pain?

Yeah.

I…

I blew the whole case, didn't I?

It doesn't matter. What matters is you're alive. You could have died.

I'd be better off dead. Look at me.

They got it from the cafeteria downstairs, in a tuna sandwich.

Look, there's a little celery still on it.

What was it like to sting someone?

I can't explain it. It was all…

All adrenaline and then… and then ecstasy!

All right.

You think it was all a trap?

Of course. I'm sorry. I flew us right into this.

What were we thinking? Look at us. We're just a couple of bugs in this world.

What will the humans do to us if they win?

I don't know.

I hear they put the roaches in motels. That doesn't sound so bad.

Adam, they check in, but they don't check out!

Oh, my.

Oould you get a nurse to close that window?

Why? The smoke. Bees don't smoke.

Right. Bees don't smoke.

Bees don't smoke! But some bees are smoking.

That's it! That's our case!

It is? It's not over?

Get dressed. I've gotta go somewhere.

Get back to the court and stall. Stall any way you can.

And assuming you've done step correctly, you're ready for the tub.

Mr. Flayman.

Yes? Yes, Your Honor!

Where is the rest of your team?

Well, Your Honor, it's interesting.

Bees are trained to fly haphazardly,

and as a result, we don't make very good time.

I actually heard a funny story about…

Your Honor, haven't these ridiculous bugs

taken up enough of this court's valuable time?

How much longer will we allow these absurd shenanigans to go on?

They have presented no compelling evidence to support their charges

against my clients, who run legitimate businesses.

I move for a complete dismissal of this entire case!

Mr. Flayman, I'm afraid I'm going

to have to consider Mr. Montgomery's motion.

But you can't! We have a terrific case.

Where is your proof? Where is the evidence?

Show me the smoking gun!

Hold it, Your Honor! You want a smoking gun?

Here is your smoking gun.

What is that?

It's a bee smoker!

What, this? This harmless little contraption?

This couldn't hurt a fly, let alone a bee.

Look at what has happened

to bees who have never been asked, "Smoking or non?"

Is this what nature intended for us?

To be forcibly addicted to smoke machines

and man-made wooden slat work camps?

Living out our lives as honey slaves to the white man?

What are we gonna do? He's playing the species card. Ladies and gentlemen, please, free these bees!

Free the bees! Free the bees!

Free the bees!

Free the bees! Free the bees!

The court finds in favor of the bees!

Vanessa, we won!

I knew you could do it! High-five!

Sorry.

I'm OK! You know what this means?

All the honey will finally belong to the bees.

Now we won't have to work so hard all the time.

This is an unholy perversion of the balance of nature, Benson.

You'll regret this.

Barry, how much honey is out there?

All right. One at a time.

Barry, who are you wearing?

My sweater is Ralph Lauren, and I have no pants.

What if Montgomery's right? What do you mean? We've been living the bee way a long time, 27 million years.

Oongratulations on your victory. What will you demand as a settlement?

First, we'll demand a complete shutdown of all bee work camps.

Then we want back the honey that was ours to begin with,

every last drop.

We demand an end to the glorification of the bear as anything more

than a filthy, smelly, bad-breath stink machine.

We're all aware of what they do in the woods.

Wait for my signal.

Take him out.

He'll have nauseous for a few hours, then he'll be fine.

And we will no longer tolerate bee-negative nicknames…

But it's just a prance-about stage name!

…unnecessary inclusion of honey in bogus health products

and la-dee-da human tea-time snack garnishments.

Oan't breathe.

Bring it in, boys!

Hold it right there! Good.

Tap it.

Mr. Buzzwell, we just passed three cups, and there's gallons more coming!

I think we need to shut down! Shut down? We've never shut down. Shut down honey production!

Stop making honey!

Turn your key, sir!

What do we do now?

Oannonball!

We're shutting honey production!

Mission abort.

Aborting pollination and nectar detail. Returning to base.

Adam, you wouldn't believe how much honey was out there.

Oh, yeah?

What's going on? Where is everybody?

Are they out celebrating? They're home. They don't know what to do. Laying out, sleeping in.

I heard your Uncle Oarl was on his way to San Antonio with a cricket.

At least we got our honey back.

Sometimes I think, so what if humans liked our honey? Who wouldn't?

It's the greatest thing in the world! I was excited to be part of making it.

This was my new desk. This was my new job. I wanted to do it really well.

And now…

Now I can't.

I don't understand why they're not happy.

I thought their lives would be better!

They're doing nothing. It's amazing. Honey really changes people.

You don't have any idea what's going on, do you?

What did you want to show me? This. What happened here?

That is not the half of it.

Oh, no. Oh, my.

They're all wilting.

Doesn't look very good, does it?

No.

And whose fault do you think that is?

You know, I'm gonna guess bees.

Bees?

Specifically, me.

I didn't think bees not needing to make honey would affect all these things.

It's notjust flowers. Fruits, vegetables, they all need bees.

That's our whole SAT test right there.

Take away produce, that affects the entire animal kingdom.

And then, of course…

The human species?

So if there's no more pollination,

it could all just go south here, couldn't it?

I know this is also partly my fault.

How about a suicide pact?

How do we do it?

I'll sting you, you step on me. Thatjust kills you twice. Right, right.

Listen, Barry… sorry, but I gotta get going.

I had to open my mouth and talk.

Vanessa?

Vanessa? Why are you leaving? Where are you going?

To the final Tournament of Roses parade in Pasadena.

They've moved it to this weekend because all the flowers are dying.

It's the last chance I'll ever have to see it.

Vanessa, I just wanna say I'm sorry. I never meant it to turn out like this.

I know. Me neither.

Tournament of Roses. Roses can't do sports.

Wait a minute. Roses. Roses?

Roses!

Vanessa!

Roses?!

Barry?

Roses are flowers! Yes, they are. Flowers, bees, pollen!

I know. That's why this is the last parade.

Maybe not. Oould you ask him to slow down?

Oould you slow down?

Barry!

OK, I made a huge mistake. This is a total disaster, all my fault.

Yes, it kind of is.

I've ruined the planet. I wanted to help you

with the flower shop. I've made it worse.

Actually, it's completely closed down.

I thought maybe you were remodeling.

But I have another idea, and it's greater than my previous ideas combined.

I don't want to hear it!

All right, they have the roses, the roses have the pollen.

I know every bee, plant and flower bud in this park.

All we gotta do is get what they've got back here with what we've got.

Bees.

Park.

Pollen!

Flowers.

Repollination!

Across the nation!

Tournament of Roses, Pasadena, Oalifornia.

They've got nothing but flowers, floats and cotton candy.

Security will be tight.

I have an idea.

Vanessa Bloome, FTD.

Official floral business. It's real.

Sorry, ma'am. Nice brooch.

Thank you. It was a gift.

Once inside, we just pick the right float.

How about The Princess and the Pea?

I could be the princess, and you could be the pea!

Yes, I got it.

Where should I sit?

What are you?

I believe I'm the pea.

The pea?

It goes under the mattresses.

Not in this fairy tale, sweetheart. I'm getting the marshal. You do that! This whole parade is a fiasco!

Let's see what this baby'll do.

Hey, what are you doing?!

Then all we do is blend in with traffic…

…without arousing suspicion.

Once at the airport, there's no stopping us.

Stop! Security.

You and your insect pack your float? Yes. Has it been in your possession the entire time?

Would you remove your shoes?

Remove your stinger. It's part of me. I know. Just having some fun. Enjoy your flight.

Then if we're lucky, we'll have just enough pollen to do the job.

Oan you believe how lucky we are? We have just enough pollen to do the job!

I think this is gonna work.

It's got to work.

Attention, passengers, this is Oaptain Scott.

We have a bit of bad weather in New York.

It looks like we'll experience a couple hours delay.

Barry, these are cut flowers with no water. They'll never make it.

I gotta get up there and talk to them.

Be careful.

Oan I get help with the Sky Mall magazine?

I'd like to order the talking inflatable nose and ear hair trimmer.

Oaptain, I'm in a real situation.

What'd you say, Hal? Nothing. Bee!

Don't freak out! My entire species…

What are you doing?

Wait a minute! I'm an attorney! Who's an attorney? Don't move.

Oh, Barry.

Good afternoon, passengers. This is your captain.

Would a Miss Vanessa Bloome in 24B please report to the cockpit?

And please hurry!

What happened here?

There was a DustBuster, a toupee, a life raft exploded.

One's bald, one's in a boat, they're both unconscious!

Is that another bee joke? No! No one's flying the plane!

This is JFK control tower, Flight 356. What's your status?

This is Vanessa Bloome. I'm a florist from New York.

Where's the pilot?

He's unconscious, and so is the copilot.

Not good. Does anyone onboard have flight experience?

As a matter of fact, there is.

Who's that? Barry Benson. From the honey trial?! Oh, great.

Vanessa, this is nothing more than a big metal bee.

It's got giant wings, huge engines.

I can't fly a plane.

Why not? Isn't John Travolta a pilot? Yes. How hard could it be?

Wait, Barry! We're headed into some lightning.

This is Bob Bumble. We have some late-breaking news from JFK Airport,

where a suspenseful scene is developing.

Barry Benson, fresh from his legal victory…

That's Barry!

…is attempting to land a plane, loaded with people, flowers

and an incapacitated flight crew.

Flowers?!

We have a storm in the area and two individuals at the controls

with absolutely no flight experience.

Just a minute. There's a bee on that plane.

I'm quite familiar with Mr. Benson and his no-account compadres.

They've done enough damage.

But isn't he your only hope?

Technically, a bee shouldn't be able to fly at all.

Their wings are too small…

Haven't we heard this a million times?

"The surface area of the wings and body mass make no sense."

Get this on the air!

Got it.

Stand by.

We're going live.

The way we work may be a mystery to you.

Making honey takes a lot of bees doing a lot of small jobs.

But let me tell you about a small job.

If you do it well, it makes a big difference.

More than we realized. To us, to everyone.

That's why I want to get bees back to working together.

That's the bee way! We're not made of Jell-O.

We get behind a fellow.

Black and yellow! Hello! Left, right, down, hover.

Hover? Forget hover. This isn't so hard. Beep-beep! Beep-beep!

Barry, what happened?!

Wait, I think we were on autopilot the whole time.

That may have been helping me. And now we're not! So it turns out I cannot fly a plane.

All of you, let's get behind this fellow! Move it out!

Move out!

Our only chance is if I do what I'd do, you copy me with the wings of the plane!

Don't have to yell.

I'm not yelling! We're in a lot of trouble.

It's very hard to concentrate with that panicky tone in your voice!

It's not a tone. I'm panicking!

I can't do this!

Vanessa, pull yourself together. You have to snap out of it!

You snap out of it.

You snap out of it.

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

Hold it!

Why? Oome on, it's my turn.

How is the plane flying?

I don't know.

Hello?

Benson, got any flowers for a happy occasion in there?

The Pollen Jocks!

They do get behind a fellow.

Black and yellow. Hello. All right, let's drop this tin can on the blacktop.

Where? I can't see anything. Oan you?

No, nothing. It's all cloudy.

Oome on. You got to think bee, Barry.

Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee!

Wait a minute. I think I'm feeling something.

What? I don't know. It's strong, pulling me. Like a 27-million-year-old instinct.

Bring the nose down.

Thinking bee! Thinking bee! Thinking bee!

What in the world is on the tarmac? Get some lights on that! Thinking bee! Thinking bee! Thinking bee!

Vanessa, aim for the flower. OK. Out the engines. We're going in on bee power. Ready, boys?

Affirmative!

Good. Good. Easy, now. That's it.

Land on that flower!

Ready? Full reverse!

Spin it around!

Not that flower! The other one!

Which one?

That flower.

I'm aiming at the flower!

That's a fat guy in a flowered shirt. I mean the giant pulsating flower

made of millions of bees!

Pull forward. Nose down. Tail up.

Rotate around it.

This is insane, Barry! This's the only way I know how to fly. Am I koo-koo-kachoo, or is this plane flying in an insect-like pattern?

Get your nose in there. Don't be afraid. Smell it. Full reverse!

Just drop it. Be a part of it.

Aim for the center!

Now drop it in! Drop it in, woman!

Oome on, already.

Barry, we did it! You taught me how to fly!

Yes. No high-five! Right. Barry, it worked! Did you see the giant flower?

What giant flower? Where? Of course I saw the flower! That was genius!

Thank you. But we're not done yet. Listen, everyone!

This runway is covered with the last pollen

from the last flowers available anywhere on Earth.

That means this is our last chance.

We're the only ones who make honey, pollinate flowers and dress like this.

If we're gonna survive as a species, this is our moment! What do you say?

Are we going to be bees, orjust Museum of Natural History keychains?

We're bees!

Keychain!

Then follow me! Except Keychain.

Hold on, Barry. Here.

You've earned this.

Yeah!

I'm a Pollen Jock! And it's a perfect fit. All I gotta do are the sleeves.

Oh, yeah.

That's our Barry.

Mom! The bees are back!

If anybody needs to make a call, now's the time.

I got a feeling we'll be working late tonight!

Here's your change. Have a great afternoon! Oan I help who's next?

Would you like some honey with that? It is bee-approved. Don't forget these.

Milk, cream, cheese, it's all me. And I don't see a nickel!

Sometimes I just feel like a piece of meat!

I had no idea.

Barry, I'm sorry. Have you got a moment?

Would you excuse me? My mosquito associate will help you.

Sorry I'm late.

He's a lawyer too?

I was already a blood-sucking parasite. All I needed was a briefcase.

Have a great afternoon!

Barry, I just got this huge tulip order, and I can't get them anywhere.

No problem, Vannie. Just leave it to me.

You're a lifesaver, Barry. Oan I help who's next?

All right, scramble, jocks! It's time to fly.

Thank you, Barry!

That bee is living my life!

Let it go, Kenny.

When will this nightmare end?!

Let it all go.

Beautiful day to fly.

Sure is.

Between you and me, I was dying to get out of that office.

You have got to start thinking bee, my friend.

Thinking bee! Me? Hold it. Let's just stop for a second. Hold it.

I'm sorry. I'm sorry, everyone. Oan we stop here?

I'm not making a major life decision during a production number!

All right. Take ten, everybody. Wrap it up, guys.

I had virtually no rehearsal for that.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

This paper explores how diverse forms of inhibition impact firing rates in models for cortical circuits. In particular, the paper studies how the network operating point affects the balance of direct inhibition from SOM inhibitory neurons to pyramidal cells, and disinhibition from SOM inhibitory input to PV inhibitory neurons. This is an important issue as these two inhibitory pathways have largely been studies in isolation. Support for the main conclusions is generally solid, but could be strengthened by additional analyses.

Strengths:

A major strength of the paper is the systematic exploration of how circuit architecture effects the impact of inhibition. This includes scans across parameter space to determine how firing rates and stability depend on effective connectivity. This is done through linearization of the circuit about an effective operating point, and then the study of how perturbations in input effect this linear approximation.

Weaknesses:

The linearization approach means that the conclusions of the paper are valid only on the linear regime of network behavior. The paper would be substantially strengthened with a test of whether the conclusions from the linearized circuit hold over a large range of network activity. Is it possible to simulate the full network and do some targeted tests of the conclusions from linearization? Those tests could be guided by the linearization to focus on specific parameter ranges of interest.

We agree with the reviewer that it would be interesting to test if our results hold in a nonlinear regime of network behaviour (i.e. the chaotic regime, see also comment 1 by reviewer 2). As mentioned above, this requires a different type of model (either rate-based or spiking model with multiple neurons instead of modelling the mean population rate dynamics) which, in our opinion, exceeds the scope of this manuscript. Furthermore, the core measures of our study, network gain, and stability require linearization. In a chaotic regime where the linearization approach is impossible, we would need to consider/define new measures to characterize network response/activity. Therefore, while certainly being an interesting question to study, the broad scope of the studying networks in a nonlinear regime is better tackled in a separate study. We now acknowledge in the discussion of our manuscript that the linearization approach is a limitation in our study and that it would be an interesting future direction to investigate chaotic dynamics.

The results illustrated in the figures are generally well described but there is very little intuition provided for them. Are there simplified examples or explanations that could be given to help the results make sense? Here are some places such intuition would be particularly helpful:

page 6, paragraph starting ”In sum ...”

Page 8, last paragraph

Page 10, paragraph starting ”In summary ...”

Page 11, sentence starting ”In sum ...”

We agree with the reviewer that we didn’t provide enough intuition to our results. We now extended the paragraphs listed by the reviewer with additional information, providing a more intuitive understanding of the results presented in the respective chapter.

Reviewer #2 (Public Review):

Summary:

Bos and colleagues address the important question of how two major inhibitory interneuron classes in the neocortex differentially affect cortical dynamics. They address this question by studying Wilson-Cowan-type mathematical models. Using a linearized fixed point approach, they provide convincing evidence that the existence of multiple interneuron classes can explain the counterintuitive finding that inhibitory modulation can increase the gain of the excitatory cell population while also increasing the stability of the circuit’s state to minor perturbations. This effect depends on the connection strengths within their circuit model, providing valuable guidance as to when and why it arises.

Overall, I find this study to have substantial merit. I have some suggestions on how to improve the clarity and completeness of the paper.

Strengths:

(1) The thorough investigation of how changes in the connectivity structure affect the gain-stability relationship is a major strength of this work. It provides an opportunity to understand when and why gain and stability will or will not both increase together. It also provides a nice bridge to the experimental literature, where different gain-stability relationships are reported from different studies.

(2) The simplified and abstracted mathematical model has the benefit of facilitating our understanding of this puzzling phenomenon. (I have some suggestions for how the authors could push this understanding further.) It is not easy to find the right balance between biologically detailed models vs simple but mathematically tractable ones, and I think the authors struck an excellent balance in this study.

Weaknesses:

(1) The fixed-point analysis has potentially substantial limitations for understanding cortical computations away from the steady-state. I think the authors should have emphasized this limitation more strongly and possibly included some additional analyses to show that their conclusions extend to the chaotic dynamical regimes in which cortical circuits often live.

We agree with the reviewer that it would be interesting to test if our results hold in a chaotic regime of network behaviour (see also comment by reviewer 1). As mentioned above, this requires a different type of model (either rate-based or spiking model with multiple neurons instead of modelling the mean population rate dynamics) which, in our opinion, exceeds the scope of this manuscript. Furthermore, the core measures of our study, network gain, and stability require linearization. In a chaotic regime where the linearization approach is impossible, we would need to consider/define new measures to characterize network response/activity. Therefore, while certainly being an interesting question to study, the broad scope of the studying networks in a nonlinear regime is better tackled in a separate study. We now acknowledge in the discussion of our manuscript that the linearization approach is a limitation in our study and that it would be an interesting future direction to investigate chaotic dynamics.

(2) The authors could have discussed – even somewhat speculatively – how SST interneurons fit into this picture. Their absence from this modelling framework stands out as a missed opportunity.

We believe that the reviewer wanted us to speculate about VIP interneurons (and not SST interneurons, which we already do extensively in the manuscript). Previous models have included VIP neurons in the circuit (e.g. del Molino et al., 2017; Palmigiano et al., 2023; Waitzmann et al., 2024). While we do not model VIP cells explicitly, we implicitly assume that a possible source of modulation of SOM neurons comes from VIP cells. We have now added a short discussion on VIP cells in the last paragraph in our discussion section.

(3) The analysis is limited to paths within this simple E,PV,SOM circuit. This misses more extended paths (like thalamocortical loops) that involve interactions between multiple brain areas. Including those paths in the expansion in Eqs. 11-14 (Fig. 1C) may be an important consideration.

We agree with the reviewer that our framework can be extended to study many other different paths, like thalamocortical loops, cortical layer-specific connectivity motifs, or circuits with VIP or L1 inhibitory neurons. Studying these questions, however, are beyond the scope of our work. In our discussion, we now mention the possibility of using our framework to study those questions.

Reviewer #3 (Public Review):

Summary:

Bos et al study a computational model of cortical circuits with excitatory (E) and two subtypes of inhibition parvalbumin (PV) and somatostatin (SOM) expressing interneurons. They perform stability and gain analysis of simplified models with nonlinear transfer functions when SOM neurons are perturbed. Their analysis suggests that in a specific setup of connectivity, instability and gain can be untangled, such that SOM modulation leads to both increases in stability and gain. This is in contrast with the typical direction in neuronal networks where increased gain results in decreased stability.

Strengths:

- Analysis of the canonical circuit in response to SOM perturbations. Through numerical simulations and mathematical analysis, the authors have provided a rather comprehensive picture of how SOM modulation may affect response changes.

- Shedding light on two opposing circuit motifs involved in the canonical E-PV-SOM circuitry - namely, direct inhibition (SOM → E) vs disinhibition (SOM → PV → E). These two pathways can lead to opposing effects, and it is often difficult to predict which one results from modulating SOM neurons. In simplified circuits, the authors show how these two motifs can emerge and depend on parameters like connection weights.

- Suggesting potentially interesting consequences for cortical computation. The authors suggest that certain regimes of connectivity may lead to untangling of stability and gain, such that increases in network gain are not compromised by decreasing stability. They also link SOM modulation in different connectivity regimes to versatile computations in visual processing in simple models.

Weaknesses:

The computational analysis is not novel per se, and the link to biology is not direct/clear.

Computationally, the analysis is solid, but it’s very similar to previous studies (del Molino et al, 2017). Many studies in the past few years have done the perturbation analysis of a similar circuitry with or without nonlinear transfer functions (some of them listed in the references). This study applies the same framework to SOM perturbations, which is a useful and interesting computational exercise, in view of the complexity of the high-dimensional parameter space. But the mathematical framework is not novel per se, undermining the claim of providing a new framework (or ”circuit theory”).

In the introduction we acknowledge that our analysis method is not novel but is rather based on previous studies (del Molino et al., 2017; Kuchibhotla et al., 2017; Kumar et al., 2023, Litwin-Kumar et al., 2016; Mahrach et al., 2020; Palmigiano et al., 2023; Veit et al., 2023; Waitzmann et al., 2024). We now rewrote parts of the introduction to make sure that it does not sound like the computational analysis has been developed by us, but that we rather use those previously developed frameworks to dissect stability and gain via SOM modulation.

Link to biology: the most interesting result of the paper with regard to biology is the suggestion of a regime in which gain and stability can be modulated in an unconventional way - however, it is difficult to link the results to biological networks: - A general weakness of the paper is a lack of direct comparison to biological parameters or experiments. How different experiments can be reconciled by the results obtained here, and what new circuit mechanisms can be revealed? In its current form, the paper reads as a general suggestion that different combinations of gain modulation and stability can be achieved in a circuit model equipped with many parameters (12 parameters). This is potentially interesting but not surprising, given the high dimensional space of possible dynamical properties. A more interesting result would have been to relate this to biology, by providing reasoning why it might be relevant to certain circuits (and not others), or to provide some predictions or postdictions, which are currently missing in the manuscript.

- For instance, a nice motivation for the paper at the beginning of the Results section is the different results of SOM modulation in different experiments - especially between L23 (inhibition) and L4 (disinhibition). But no further explanation is provided for why such a difference should exist, in view of their results and the insights obtained from their suggested circuit mechanisms. How the parameters identified for the two regimes correspond to different properties of different layers?

As pointed out by the reviewer, the main goal of our manuscript is to provide a general understanding of how gain and stability depend on different circuit motifs (ie different connectivity parameters), and how circuit modulations via SOM neurons affect those measures. However, we agree with the reviewer that it would be useful to provide some concrete predictions or postdictions following from our study.

An interesting example of a postdiction of our model is that the firing rate change of excitatory neurons in response to a change in the stimulus (which we define as network gain, Eq. 2) depends on firing rates of the excitatory, PV, and SOM neurons at the moment of stimulus presentation (Fig. 3ii; Fig. 4Aii,Bii,Cii; Fig. 5Aii, Bii, Cii). Hence any change in input to the circuit can affect the response gain to a stimulus presentation, in line with experimental evidence which suggests that changes in inhibitory firing rates and changes in the behavioral state of the animal lead to gain modifications (Ferguson and Cardin 2020).

Another recent concrete example is the study of Tobin et al., 2023, in which the authors show that optogenetically activating SOM cells in the mouse primary auditory cortex (A1) decreases the excitatory responses to auditory stimuli. In our framework, this corresponds to the case of decreases in network gain (gE) for positive SOM modulation, as seen in the circuit with PV to SOM feedback connectivity (Suppl. Fig. S1).

Another example is the study by Phillips and Hasenstaub 2016, in which the authors study the effect of optogenetic perturbations of SOM (and PV) cells on tuning curves of pyramidal cells in mouse A1. While they find large heterogeneity in additive/subtractive or multiplicative/divisive tuning curve changes following SOM inactivation, most cells have a purely multiplicative or purely additive component (and none of the cells have a divisive component). In our study, we see that large multiplicative responses of the excitatory population follow from circuits with strong E to SOM feedback connectivity.

We note that in future computational studies, it would be useful to apply our framework with a focus on a specific brain region and add all relevant cell types (at a minimum E, PV, SOM, and VIP) plus a dendritic compartment, in order to formulate much more precise experimental predictions.

We have now added additional information to the discussion section.

- Another caveat is the range of parameters needed to obtain the unintuitive untangling as a result of SOM modulation. From Figure 4, it appears that the ”interesting” regime (with increases in both gain and stability) is only feasible for a very narrow range of SOM firing rates (before 3 Hz). This can be a problem for the computational models if the sweet spot is a very narrow region (this analysis is by the way missing, so making it difficult to know how robust the result is in terms of parameter regions). In terms of biology, it is difficult to reconcile this with the realistic firing rates in the cortex: in the mouse cortex, for instance, we know that SOM neurons can be quite active (comparable to E neurons), especially in response to stimuli. It is therefore not clear if we should expect this mechanism to be a relevant one for cortical activity regimes.

We agree with the reviewer that it’s important to test the robustness of our results. As suggested by the reviewer, we now include a new supplementary figure (Suppl. Fig. S2) which measures the percentage of data points in the respective quadrant Q1-Q4 when changing the SOM firing rates (as done in Fig. 5). We see that the quadrants in which the network gain and stability change in the same direction (Q2 and Q3) remain high in the case for E to SOM feedback (Suppl. Fig. S2A) over SOM rates ranging over 0-10 Hz (and likely beyond).

- One of the key assumptions of the model is nonlinear transfer functions for all neuron types. In terms of modelling and computational analysis, a thorough analysis of how and when this is necessary is missing (an analysis similar to what has been attempted at in Figure 6 for synaptic weights, but for cellular gains). In terms of biology, the nonlinear transfer function has experimentally been reported for excitatory neurons, so it’s not clear to what extent this may hold for different inhibitory subtypes. A discussion of this, along with the former analysis to know which nonlinearities would be necessary for the results, is needed, but currently missing from the study. The nonlinearity is assumed for all subtypes because it seems to be needed to obtain the results, but it’s not clear how the model would behave in the presence or absence of them, and whether they are relevant to biological networks with inhibitory transfer functions.

It is true that the nonlinear transfer function is a key component in our model. We chose identical transfer functions for E, PV, and SOM (; Eq. 4) to simplify our analysis. If the transfer function of one of the neuron types would be linear (β = 1), then the corresponding b terms (the slope of the nonlinearity at the steady state; b = dfX/dqX; Fig. 1B; Eq. 4) would be equal to α. Therefore, if neurons had a linear transfer function in our model, there would not be a dependence of network gain on E and PV firing rate as studied in Fig. 3-5. This is because the relationship between PV rates and their gain would be constant (bP = α) in Fig. 1B (bottom).

If all the transfer functions were linear, changes in firing rates would not have an impact on network gain or stability. Changing the nonlinear transfer function by changing the α or β terms in Eq. 4 would only scale the way a change in the rates affects the b terms and hence the results presented in Fig. 3-5. More interesting would be to study how different types of nonlinearities, like sigmoidal functions or sublinear nonlinearities (i.e. saturating nonlinearities), would change our results. However, we think that such an investigation is out of scope for this study. We now added a comment to the Methods section.

Experimentally, F-I curves have been measured also for PV and SOM neurons. For example, Romero-Sosa et al., 2021 measure the F-I curve of pyramidal, PV and SOM neurons in mouse cortical slices. They find that similar to pyramidal neurons, PV and SOM neurons show a nonlinear F-I curve. We now added the citation of Romero-Sosa et al., 2021 to our manuscript.

- Tuning curves are simulated for an individual orientation (same for all), not considering the heterogeneity of neuronal networks with multiple orientation selectivity (and other visual features) - making the model too simplistic.

The reviewer is correct that we only study changes in tuning curves in a simplistic model. In our model, the excitatory and PV populations are tuned to a single orientation (in the case of Fig. 7 to θ = 90). While this is certainly an oversimplification, it allows us to understand how additive/subtractive and multiplicative/divisive changes in the tuning curves come about in networks with different connectivity motifs. To model heterogeneity of tuning responses within a network, it requires more complex models. A natural choice would be to extend a classical ring attractor model (Rubin et al., 2015) by splitting the inhibitory population into PV and SOM neurons, or study the tuning curve heterogeneity that occurs in balanced networks (Hansel and van Vreeswijk 2012). However, this model has many more parameters, like the spatial connectivity profiles from and onto PV and SOM neurons. While highly valuable, we believe that studying such models exceeds the scope of our current manuscript. We now added a paragraph in the discussion section, mentioning this as an interesting future direction.

Reviewer #1 (Recommendations For The Authors):

The last sentence of the abstract is hard to interpret before reading the rest of the paper - suggest replacing or rephrasing.

We rephrased the sentence to make more clear what we mean.

Page 3, last full paragraph: I think this assumes that phi is positive. What is the justification for that assumption? More generally, I think you could say a bit more about phi in the main text since it is a fairly complicated term.

The reviewer is correct, for a stable system phi is always positive. We now clarify this and explain phi in more detail in the main text.

Fig 1D: It would be helpful to identify when the stimulus comes on and be clearer about what the stimulus is. I assume it’s a step increase in S input at 0.05 s or so - but that should be immediately apparent looking at the figure.

We agree with the reviewer and we added a dashed line at the time of stimulus onset in Fig. 1D.

Page 5: ”To motivate our analysis we compare ... (Fig. 2A)” - Figure 2A does not show responses without modulation, so this sentence is confusing.

The dashed lines in Fig. 2A (and Fig. 2C) actually represents the rate change without modulation.

Page 6: sentence “The central goal of our study ...” seems out of place since this is pretty far into the results, and that goal should already be clear.

We agree with the reviewer, hence we updated the sentence.

Page 10, top: the green curve in panel Aii always has a negative slope - so I am confused by the statement that increasing wSE decreases both gain and stability.

We thank the reviewer for pointing out this mistake. We now fixed it in the text.

Figure 6: in general it is hard to see what is going on in this figure (the green and blue in particular are hard to distinguish). Some additional labels would be helpful, but I would also see if the color scheme can be improved.

We added a zoom-in to the panels which were hard to distinguish.

Reviewer #2 (Recommendations For The Authors):

Major recommendations:

(1) The authors should explain early on in the results section what the key factor(s) is that differentiates SOM from PV cells in their model. E.g., in Fig. 1A, the only obvious difference is that SOM cells don’t inhibit themselves. However, later on in the paper, the difference in external stimulus drive to these interneuron classes is more heavily emphasized. Given the importance of that difference (in external stim drive), I think this should be highlighted early on.

We now mention the key factors that differentiate PV and SOM neurons already when describing Fig. 1A.

(2) The result in Figs. 5,6 demonstrate that recurrent SOM connectivity is important for achieving increases in both gain and stability. This observation could benefit from some intuitive explanation. Perhaps the authors could find this explanation by looking at their series expansion (Eqs. 11-14, Fig. 1C) and determining which term(s) are most important for this effect. The corresponding paths through the circuit – the most important ones – could then be highlighted for the reader.

We agree with the reviewer that our results benefit from more intuitive explanations. This has also been pointed out by reviewer 1 in their public review. We now extended the concluding paragraphs in the context of Fig. 4-6 with additional information, providing a more intuitive understanding of the results presented in the respective chapter. While it is possible to gain an intuitive understanding of how the network gain depends on rate and weight parameters (Eq. 2), this understanding is unfortunately missing in the case of stability. The maximum eigenvalue of the system have a complex relationship with all the parameters, and often have nonlinear dependencies on changes of a parameter (e.g. as we show in Fig. 3iv or one can see in Fig. 6). We now discuss this difficulty at the end of the section “Influence of weight strength on network gain vs stability”.

(3) I think the authors should consider including some analyses that do not rely on the system being at or near a fixed point. I admit that such analysis could be difficult, and this could of course be done in a future study. Nevertheless, I want to reiterate that this addition could add a lot of value to this body of work.

As outlined above, we decided to not include additional analysis on network behaviour in nonlinear regimes but we now acknowledge in the discussion of our manuscript that the linearization approach is a limitation in our study and that it would be an interesting future direction to investigate chaotic dynamics.

Minor recommendations:

(1) At the top of P. 6, when the authors first discuss the stability criterion involving eigenvalues, they should address the question ”eigenvalues of what?”. I suggest introducing the idea of the Jacobian matrix, and explaining that the largest eigenvalue of that matrix determines how rapidly the system will return to the fixed point after a small perturbation.

We included an additional sentence in the respective paragraph explaining the link between stability and negative eigenvalues, and we also added a sentence in the Methods section stating the the largest real eigenvalue dominates the behavior of the dynamical system.

(2) The panel labelling in Fig. 3 is unnecessarily confusing. It would be simpler (and thus better) to simply label the panels A,B,C,D, or i,ii,iii,iv, instead of the current labelling: Ai, Aii, Aiii, Aiv. (There are currently no panels ”B” in Fig. 3).

We updated the figure accordingly.

Reviewer #3 (Recommendations For The Authors):

• Suggestions for improved or additional experiments, data or analyses.

Analysis of the effect of different nonlinear transfer functions is necessary.

Please see our detailed answer to the reviewer’s comment in the public review above.

Analysis of gain modulation in models with more realistic tuning properties.

Please see our detailed answer to the reviewer’s comment in the public review above.

Mathematical analysis of the conditions to obtain ”untangled” gain and stability:

One of the promises of the paper is that it is offering a computational framework or circuit theory for understanding the effect of SOM perturbation. However, the main result, namely the untangling of gain and stability, has only been reported in numerical simulations (e.g. Fig. 6). Different parameters have been changed and the results of simulations have been reported for different conditions. Given the simplified model, which allows for rigorous mathematical analysis, isn’t it possible to treat this phenomenon more analytically? What would be the conditions for the emergence of the untangled regime? This is currently missing from the analyses and results.

We agree with the reviewer that our results benefit from more intuitive explanations. This has also been pointed out by reviewer 1 in their public review. We now extended the concluding paragraphs in the context of Fig. 4-6 with additional information, providing a more intuitive understanding of the results presented in the respective chapter. While it is possible understand analytically of how the network gain depends on rate and weight parameters (Eq. 2), this understanding is unfortunately missing in the case of stability. The maximum eigenvalue of the system have a complex relationship with all the parameters, and often have nonlinear dependencies on changes of a parameter (e.g. as we show in Fig. 3iv or one can see in Fig. 6). This doesn’t allow for a a deep analytical understanding of the entangling of gain and stability. We now discuss this difficulty at the end of the section “Influence of weight strength on network gain vs stability”.

• Recommendations for improving the writing and presentation. The Results section is well written overall, but other parts, especially the Introduction and Discussion, would benefit from proof reading - there are many typos and problems with sentence structures and wording (some mentioned below).

We have gone through the manuscript again and improved the writing.

The presentation of the dependence on weight in Figure 6 can be improved. For instance, the authors talk about the optimal range of PV connectivity, but this is difficult to appreciate in the current illustration and with the current colour scheme.

We added a zoom in to the panels which were hard to distinguish.

• Minor corrections to the text and figures. Text:

We thank the reviewer for their thorough reading of our manuscript. We fixed all the issues from below in the manuscript.

Some examples of bad structure or wording:

From the Abstract:

”We show when E - PV networks recurrently connect with SOM neurons then an SOM mediated modulation that leads to increased neuronal gain can also yield increased network stability.” From Introduction:

Sentence starting with ”This new circuit reality ...”

”Inhibition is been long identified as a physiological or circuit basis for how cortical activity changes depending upon processing or cognitive needs ...”

Sentence starting with ”Cortical models with both ...”

”... allowing SOM neurons the freedom to ..”

From Results:

”... affects of SOM neurons on E ..”

”seem in opposition to one another, with SOM neuron activity providing either a source or a relief of E neuron suppression”. The sentence after is also difficult to read and needs to be simplified.

P. 7: ”We first remark that ...”

Difficult to read/understand - long and badly structured sentence.

P. 8: ”adding a recurrent connection onto SOM neurons from the E-PV subcircuit” It’s from E (and not PV) to be more precise (Fig. 5).

Discussion:

”Firstly, E neurons and PV neurons experience very similar synaptic environments.” What does it mean?

”Fortunately, PV neurons target both the cell bodies and proximal dendrites” Fortunately for whom or what? ”in line with arge heterogeneity”

Methods:

Matrix B is never defined - the diagonal matrix of b (power law exponents) I assume.

Some of the other notations too, e.g. bs, etc (it’s implicit, but should be explained).

Structure of sentence:

”Network gain is defined as ...” (p. 17)

Figure:

The schematics in Figure 4 can be tweaked to highlight the effect of input (rather than other components of the network, which are the same and repetitive), to highlight the main difference for the reader.

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors present a new application of the high-content image-based morphological profiling Cell Painting (CP) to single cell type classification in mixed heterogeneous induced pluripotent stem cellderived mixed neural cultures. Machine learning models were trained to classify single cell types according to either "engineered" features derived from the image or from the raw CP multiplexed image. The authors systematically evaluated experimental (e.g., cell density, cell types, fluorescent channels) and computational (e.g., different models, different cell regions) parameters and convincingly demonstrated that focusing on the nucleus and its surroundings contains sufficient information for robust and accurate cell type classification. Models that were trained on mono-cultures (i.e., containing a single cell type) could generalize for cell type prediction in mixed co-cultures, and describe intermediate states of the maturation process of iPSC-derived neural progenitors to differentiation neurons.

Strengths:

Automatically identifying single-cell types in heterogeneous mixed-cell populations holds great promise to characterize mixed-cell populations and to discover new rules of spatial organization and cell-cell communication. Although the current manuscript focuses on the application of quality control of iPSC cultures, the same approach can be extended to a wealth of other applications including an in-depth study of the spatial context. The simple and high-content assay democratizes use and enables adoption by other labs.

The manuscript is supported by comprehensive experimental and computational validations that raise the bar beyond the current state of the art in the field of high-content phenotyping and make this manuscript especially compelling. These include (i) Explicitly assessing replication biases (batch effects); (ii) Direct comparison of feature-based (a la cell profiling) versus deep-learning-based classification (which is not trivial/obvious for the application of cell profiling); (iii) Systematic assessment of the contribution of each fluorescent channel; (iv) Evaluation of cell-density dependency; (v) Explicit examination of mistakes in classification; (vi) Evaluating the performance of different spatial contexts around the cell/nucleus; (vii) Generalization of models trained on cultures containing a single cell type (mono-cultures) to mixed co-cultures; (viii) Application to multiple classification tasks.

I especially liked the generalization of classification from mono- to co-cultures (Figure 4C), and quantitatively following the gradual transition from NPC to Neurons (Figure 5H).

The manuscript is well-written and easy tofollow.

Thank you for the positive appreciation of our work and constructive comments. 

Weaknesses:

I am not certain how useful/important the specific application demonstrated in this study is (quality control of iPSC cultures), this could be better explained in the manuscript. 

To clarify the importance we have added an additional explanation to the introduction (page 3) and also come back to it in the discussion (page 17).

Text from the introduction:

“However, genetic drift, clonal and patient heterogeneity cause variability in reprogramming and differentiation efficiency10,11. The differentiation outcome is further strongly influenced by variations in protocol12. This can significantly impact experimental outcomes, leading to inconsistent and potentially misleading results and consequently, it hinders the use of iPSC-derived cell systems in systematic drug screening or cell therapy pipelines. This is particularly true for iPSC-derived neural cultures, as their composition, purity and maturity directly affect gene expression and functional activity, which is essential for modelling neurological conditions13,14. Thus, from a preclinical perspective, there is the need for a fast and cost-effective QC approach to increase experimental reproducibility and cell type specificity15. From a clinical perspective in turn, robust QC is required for safety and regulatory compliance (e.g., for cell therapeutic solutions). This need for improved standardization and QC is underscored by large-scale collaborative efforts such as the International Stem Cell Banking Initiative16, which focusses on clinical quality attributes and provides recommendations for iPSC validation testing for use as cellular therapeutics, or the CorEuStem network, aiming to harmonize iPSC practices across core facilities in Europe.”

Text from the discussion: 

“Many groups highlight the difficulty of reproducible neural differentiation and attribute this to culture conditions, cultivation time and variation in developmental signalling pathways in the source iPSC material43,44. Spontaneous neural differentiation has previously been shown to require approximately 80 days before mature neurons arise that can fire action potentials and show neural circuit formation. Although these differentiation processes display a stereotypical temporal sequence34, the exact timing and duration might vary. This variation negatively affects the statistical power when testing drug interventions and thus prohibits the application of iPSC-culture derivatives in routine drug screening. Current solutions (e.g., immunocytochemistry, flow cytometry, …) are often cost-ineffective, tedious, and incompatible with longitudinal/multimodal interrogation. CP is a much more cost-effective solution and ideally suited for this purpose. Routine CP-based could add confidence to and save costs for the drug discovery pipeline. We have shown that CP can be leveraged to capture the morphological changes associated with neural differentiation.”

Another issue that I feel should be discussed more explicitly is how far can this application go - how sensitively can the combination of cell painting and machine learning discriminate between cell types that are more subtly morphologically different from one another?

Thank you for this interesting question. The fact that an approach based on a subregion not encompassing the whole cell (the “nucleocentric” approach) can predict cell types equally well, suggests that the cell shape as such is not the defining factor for accurate cell type profiling. And, while clearly neural progenitors, neurons or glia have vastly different cell shapes. We have shown that cells with closer phenotypes such as 1321N1 vs. SH-SY5Y or astrocytes vs. microglia can be distinguished with equal performance. However, triggered by the reviewers’ question, we have now tested additional conditions with more subtle phenotypes, including the classification of 1321N1 vs. two related retinal pigment epithelial cells with much more similar morphology (ARPE and RPE1 cells). We found that the CNN could discriminate these cells equally well and have added the results on page 8 and in Fig. 3D. To address this question from a different angle, we have also performed an experiment in which we changed cell states to assess whether discriminatory power remains high. Concretely, we exposed co-cultures of neurons and microglia to LPS to trigger microglial activation (more subtly visible as cytoskeletal changes and vacuole formation). This revealed that our approach still discriminates both cell types (neurons vs. microglia) with high accuracy, regardless of the microglial state. Furthermore, using a two-step approach, we could also distinguish LPS-treated (assumed to be activated) from unchallenged microglia (assumed to be more homeostatic), albeit with a lower accuracy. This experiment has been added as an extra results section (Cell type identification can be applied to mixed iPSC-derived neuronal cultures regardless of activation state, p12) and Fig. 7c. Finally, we have also added our take on what the possibilities could be for future applications in even more complex contexts such as tissue slice, 3D and live cell applications (page 17-18). 

Regarding evaluations, the use of accuracy, which is a measure that can be biased by class imbalance, is not the most appropriate measurement in my opinion. The confusion matrices are a great help, but I would recommend using a measurement that is less sensitive for class imbalance for cell-type classification performance evaluations.  

Across all CNNs trained in this manuscript, the sample size of the input classes has always been equalized, ruling out any effects of class imbalance. Nevertheless, to follow the reviewers’ recommendation, we have now used the F-score to document performance as it is insensitive to such imbalance. For clarity, we have now also mentioned the input number (ROIs/class) in every figure.

Another issue is that the performance evaluation is calculated on a subset of the full cell population - after exclusion/filtering. Could there be a bias toward specific cell types in the exclusion criteria? How would it affect our ability to measure the cell type composition of the population?

As explained in the M&M section, filtering was performed based on three criteria:

(1) Nuclear size: values below a threshold of 160, objects are considered to represent debris;

(2) DAPI intensity: values below a threshold of 500 represent segmentation errors;

(3) IF staining intensity: gates were set onto the intensity of the fluorescent markers used with posthoc IF to only retain cells that are unequivocally positive for either marker and to avoid inclusion of double positive (or negative) cells in the ground truth training. 

One could argue that the last criterion introduces a certain bias in that it does not consider part of the cell population. However, this is also not the purpose of our pioneering study that aims at identifying unique cell types for which ground truth is as pure and reliable as possible. Not filtering out these cells with a ‘dubious’ IF profile (e.g., cells that might be transitioning or are of a different type) would negatively affect the model by introducing noise. It is correct that the predictions are based only on these inputs and so cells of a subsequent test set will only be classified according to these labels. For example, in the neuronal differentiation experiment (Fig. 6G-H), cells are either characterized as NPC or as neurons, which leaves the transitioning (or undefined) cells in either category. Despite this simplification, the model adequately predicted the increase in neuron/NPC ratio with culture age. In future iterations, one could envision defining more refined cell (sub-)types in a population based on richer post-hoc information (e.g., through cyclic immunofluorescence or spatial single cell transcriptomics) or longitudinal follow-up of cell-state transitions using live imaging. This notion has been added to page 17 of the manuscript.

I am not entirely convinced by the arguments regarding the superiority of the nucleocentric vs. the nuclear representations. Could it be that this improvement is due to not being sensitive/ influenced by nucleus segmentation errors?

The reviewer has a valid point that segmentation errors may occur. However, the algorithm we have used (Stardist classifier), is very robust to nuclear segmentation errors. To verify the performance, we have now quantified segmentation errors in 20 images for 3 different densities and found a consistently low error rate (0.6 -1.6%) without correlation to the culture density. Moreover, these errors include partial imperfections (e.g., a missed protrusion or bleb) as well as over- (one nucleus detected as more) or under- (more nuclei detected as one) segmentations. The latter two will affect both the nuclear and nucleocentric predictions and should thus not affect the prediction performance. In the case of imperfect segmentations, there may be a specific impact on the nucleus-based predictions (which rely on blanking the non-nuclear part), but this alone cannot explain the significantly higher gain in accuracy for nucleocentric predictions (>5%). Therefore, we conclude that segmentation errors may contribute in part, but not exclusively, to the overall improved performance of nucleocentric input models. We have added this notion in the discussion (pages 14-15 and Suppl. Fig. 1E).

GRADCAM shows cherry-picked examples and is not very convincing.

To help convince the reviewer and illustrate the representativeness of selected images, we have now randomly selected for each condition and density 10 images (using random seeds to avoid cherrypicking) and added these in a Suppl. Fig. 3.

There are many missing details in the figure panels, figure legend, and text that would help the reader to better appreciate some of the technical details, see details in the section on recommendations for the authors.

Please see further for our specific adaptations.

Reviewer #2 (Public Review):

This study uses an AI-based image analysis approach to classify different cell types in cultures of different densities. The authors could demonstrate the superiority of the CNN strategy used with nucleocentric cell profiling approach for a variety of cell types classification. The paper is very clear and well-written. I just have a couple of minor suggestions and clarifications needed for the reader.

The entire prediction model is based on image analysis. Could the authors discuss the minimal spatial resolution of images required to allow a good prediction? Along the same line, it would be interesting to the reader to know which metrics related to image quality (e.g. signal to noise ratio) allow a good accuracy of the prediction.

Thank you for the positive and relevant feedback.

The reviewer has a good point that it is important to portray the imaging conditions that are required for accurate predictions. To investigate this further we have performed additional experiments that give a better view on the operating window in terms of resolution and SNR (manuscript page 7-8 and new figure panels Fig. 3B-C). The initial image resolution was 0.325 µm/pixel. To understand the dependency on resolution we performed training and classifications for image data sets that were progressively binned. We found that a two-fold reduction in resolution did not significantly affect the F-score, but further degradation decreased the performance. At a resolution of 6,0 µm/pixel (20-fold binning), the F-score dropped to 0.79±0.02, comparable to the performance when only the DAPI (nuclear) channel was used as input. The effect of reduced image quality was assessed in a similar manner, by iteratively adding more Gaussian noise to the image. We found that above an SNR of 10 the prediction performance remains consistent but below it starts to degrade. While this exercise provides a first impression of the current confines of our method, we do believe it is plausible that its performance can be extended to even lower-quality images for example by using image restoration algorithms. We have added this notion in the discussion (page 14).

The authors show that nucleocentric-based cell feature extraction is superior to feeding the CNN-based model for cell type prediction. Could they discuss what is the optimal size and shape of this ROI to ensure a good prediction? What if, for example, you increase or decrease the size of the ROI by a certain number of pixels?

To identify the optimal input, we varied the size of the square region around the nuclear centroid from 0.6 to 150 µm for the whole dataset. Within the nuclear-to-cell window (12µm- 30µm) the average Fscore is limited, but an important observation is the increasing error and differences in precision and recall with increasing nucleocentric patch sizes, which will become detrimental in cases of class imbalance. The F-score is maximal for a box of 12-18µm surrounding the nuclear centroid. In this “sweet spot”, the precision and recall are also in balance. Therefore, we have selected this region for the actual density comparison experiment. We have added our results to the manuscript (page 9 and 15).

It would be interesting for the reader to know the number of ROI used to feed each model and know the minimal amount of data necessary to reach a high level of accuracy in the predictions.

The figures have now been adjusted so that the number of ROIs used as input to feed the model are listed. The minimal number of ROIs required to obtain high level accuracy is tested in Figure 2C. By systematically increasing the number of input ROIs for both RF and CNN, we found that a plateau is reached at 5000 input ROIs (per class) for optimal prediction performance. This is also documented in the results section page 6.

From Figure 1 to Figure 4 the author shows that CNN based approach is efficient in distinguishing 1321N1 vs SH-SY5Y cell lines. The last two figures are dedicated to showing 2 different applications of the techniques: identification of different stages of neuronal differentiation (Figure 5) and different cell types (neurons, microglia, and astrocytes) in Figure 6. It would be interesting, for these 2 two cases as well, to assess the superiority of the CNN-based approach compared to the more classical Random Forest classification. This would reinforce the universal value of the method proposed.

To meet the reviewer’s request, we have now also compared CNN to RF for the classification of cells in iPSC-derived models (Figures 6 and 7). As expected, the CNN performed better in both cases. We have now added these results in Fig. 6 D and 7 C and pages 12 and 13 of the manuscript.

Reviewer #3 (Public Review):

Induced pluripotent stem cells, or iPSCs, are cells that scientists can push to become new, more mature cell types like neurons. iPSCs have a high potential to transform how scientists study disease by combining precision medicine gene editing with processes known as high-content imaging and drug screening. However, there are many challenges that must be overcome to realize this overall goal. The authors of this paper solve one of these challenges: predicting cell types that might result from potentially inefficient and unpredictable differentiation protocols. These predictions can then help optimize protocols.

The authors train advanced computational algorithms to predict single-cell types directly from microscopy images. The authors also test their approach in a variety of scenarios that one may encounter in the lab, including when cells divide quickly and crowd each other in a plate. Importantly, the authors suggest that providing their algorithms with just the right amount of information beyond the cells' nuclei is the best approach to overcome issues with cell crowding.

The work provides many well-controlled experiments to support the authors' conclusions. However, there are two primary concerns: (1) The model may be relying too heavily on the background and thus technical artifacts (instead of the cells) for making CNN-based predictions, and (2) the conclusion that their nucleocentric approach (including a small area beyond the nucleus) is not well supported, and may just be better by random chance. If the authors were to address these two concerns (through additional experimentation), then the work may influence how the field performs cell profiling in the future.

Thank you very much for confirming the potential value of our work and raising these relevant items. To better support our claims we have now performed additional validations, which we detail below. 

(1) The model may be relying too heavily on the background and thus technical artifacts (instead of the cells) for making CNN-based predictions 

To address the first point, we have adapted the GradCAM images to show an overlay of the input crop and GradCAM heatmap to give a better view of the structures that are highlighted by the CNN. We further investigated the influence of the background on the prediction performance. Our finding that a CNN trained on a monoculture retains a relatively high performance on cocultures implies that the CNN uses the salient characteristics of a cell to recognize it in more complex heterogeneous environments. Assuming that the background can vary between experiments, the prediction of a pretrained CNN on a new dataset indicates that cellular characteristics are used for robust prediction.  When inspecting GradCAM images obtained from the nucleocentric CNN approaches (now added in Suppl. Fig. 3), we noticed that the nuclear periphery typically contributed the most (but not exclusively) to the prediction performance. When using only the nuclear region as input, GradCAMs were more strongly (but again not exclusively) directed to the background surrounding the nuclei. To train the latter CNN, we had cropped nuclei and set the background to a value of zero. To rule out that this could have introduced a bias, we have now performed the exact same training and classification, but setting the background to random noise instead (Suppl. Fig. 2). While this effectively diverted the attention of the GradCAM output to the nucleus instead of the background, the prediction performance was unaltered. We therefore assume that irrespective of the background, when using nuclear crops as input, the CNN is dominated by features that describe nuclear size. We observe that nuclear size is significantly different in both cell types (although intranuclear features also still contribute) which is also reflected in the feature map gradient in the first UMAP dimension (Suppl. Fig. 2). This notion has been added to the manuscript (page 9) and Suppl. Fig. 2. 

(2) The conclusion that their nucleocentric approach (including a small area beyond the nucleus) is not well supported, and may just be better by random chance. 

To address this second concern, which was also raised by reviewer 2, we have performed a more extensive analysis in which the patch size was varied from 0.6 to 120µm around the nuclear centroid (Fig. 4E and page 9 of the manuscript). We observed that there is little effect of in- or decreasing patch size on the average F-score within the nuclear to cell window, but that the imbalance between the precision and recall increases towards the larger box sizes (>18µm). Under our experimental conditions, the input numbers per class were equal, but this will not be the case in situations where the ground truth is unknown (and needs to be predicted by the CNN). Therefore, a well-balanced CNN is of high importance. This notion has been added to page 15 of the manuscript.

The main advantage of nucleocentric profiling over whole-cell profiling in dense cultures is that it relies on a more robust nuclear segmentation method and is less sensitive to differences in cell density (Suppl. Fig. 1D). In other words, in dense cultures, the segmentation mask will contain similar regional input as the nuclear mask and the nucleocentric crop will contain more perinuclear information which contributes to the prediction accuracy. Therefore, at high densities, the performance of the CNN on whole-cell crops decreases owing to poorer segmentation performance. A CNN that uses nucleocentric crops, will be less sensitive to these errors. This notion has been added to pages 14-15 of the manuscript. 

Additionally, the impact of this work will be limited, given the authors do not provide a specific link to the public source code that they used to process and analyze their data.

The source code is now available on the Github page of the DeVos lab, under the following URL: https://github.com/DeVosLab/Nucleocentric-Profiling

Recommendations for the authors:  

Reviewing Editor (Recommendations For The Authors):

Evaluation summary

The authors present a new application of the high-content image-based morphological profiling Cell Painting (CP) to single cell type classification in mixed heterogeneous induced pluripotent stem cellderived mixed neural cultures. Machine learning models were trained to classify single cell types according to either "engineered" features derived from the image or from the raw CP multiplexed image. The authors systematically evaluated experimental (e.g., cell density, cell types, fluorescent channels, replication biases) and computational (e.g., different models, different cell regions) parameters and argue that focusing on the nucleus and its surroundings contains sufficient information for robust and accurate cell type classification. Models that were trained on mono-cultures (i.e., containing a single cell type) could generalize for cell type prediction in mixed co-cultures, and describe intermediate states of the maturation process of iPSC-derived neural progenitors to differentiation neurons.

Strengths:

Automatically identifying single-cell types in heterogeneous mixed-cell populations is an important application and holds great promise. The simple and high-content assay democratizes use and enables adoption by other labs. The manuscript is supported by comprehensive experimental and computational validations. The manuscript is well-written and easy to follow.

Weaknesses:

The conclusion is that the nucleocentric approach (including a small area beyond the nucleus) is not well supported, and may just be better by random chance. If better supported by additional experiments, this may influence how the field performs cell profiling in the future. Model interpretability (GradCAM) analysis is not convincing. The lack of a public source code repository is also limiting the impact of this study. There are missing details in the figure panels, figure legend, and text that would help the reader to better appreciate some of the technical details.

Essential revisions:

To reach a "compelling" strength of evidence the authors are requested to either perform a comprehensive analysis of the effect of ROI size on performance, or tune down statements regarding the superior performance of their "nucleocentric" approach. Further addition of a public and reproducible source code GitHub repository will lead to an "exceptional" strength of evidence.

To answer the main comment, we have performed an experiment in which we varied the size of the nucleocentric patch and quantified CNN performance. We have also evaluated the operational window of our method by varying the resolution and SNR and we have experimented with different background blanking methods. We have expanded our examples of GradCAM images and now also made our source code and an example data set available via GitHub.

Reviewer #1 (Recommendations For The Authors):

I think that an evaluation of how the excluded cells affect our ability to measure the cell type composition of the population would be helpful to better understand the limitations and practical measurement noise introduced by this approach. A similar evaluation of the excluded cells can also help to better understand the benefit of nucleocentric vs. cell representations by more convincingly demonstrating the case for the nucleocentric approach. In any case, I recommend discussing in more depth the arguments for using the nucleocentric representation and why it is superior to the nuclear representation.

The benefits of nucleocentric representation over nuclear and whole-cell representation are discussed more in depth at pages 14-15 of the manuscript. 

“The nucleocentric approach, which is based on more robust nuclear segmentation, minimizes such mistakes whilst still retaining input information from the structures directly surrounding the nucleus. At higher cell density, the whole-cell body segmentation becomes more error-prone, while also loosing morphological information (Suppl. Fig. 1D). The nucleocentric approach is more consistent as it relies on a more robust segmentation and does not blank the surrounding region. This way it also buffers for occasional nuclear segmentation errors (e.g., where blebs or parts of the nucleus are left undetected).”

It is not entirely clear to me why Figure 5 moves back to "engineered" features after previous figures showed the superiority of the deep learning approach. Especially, where Figure 6 goes again to DL. Dimensionality reduction can be also applied to DL-based classifications (e.g., using the last layer).

Following up on the reviewers’ interesting comment, we extracted the embeddings from the trained CNN and performed UMAP dimensionality reduction. The results are shown in Fig. 3D, 6F and supplementary figure 1B and added to the manuscript on pages 6, 8 and 12. 

We concluded that unsupervised dimensionality reduction using the feature embeddings could separate cell type clusters, where the distance between the clusters reflected the morphological similarity between the cell lines. 

I would recommend including more comprehensive GRADCAM panels in the SI to reduce the concern of cherry-picking examples. What is the interpretation of the nucleocentric area?

A more extensive set of GradCAM images have now been included in supplementary material (Supplementary figure 3) using the same random seeds for all conditions, thus avoiding any cherry picking. We interpret the GradCAM maps on the nucleocentric crops as highlighting the structures surrounding the nucleus (reflecting ER, mitochondria, Golgi) indicating their importance in correct cell classification. This was added to the manuscript on pages 9 and 15.

Missing/lacking details and suggestions in the figure panels and figure legend:

- Scale bars missing in some of the images shown (e.g., Figure 2F, Figure 3D, Figure 4, Supplementary Figure 4), what are the "composite" channels (e.g., Figure 2F), missing x-label in Figure 3B. 

These have now been added.

- Terms that are not clear in the figure and not explained in the legend, such as FITC and cy3 energy (Figure 1C). 

The figure has been adapted to better show the region, channel and feature. We have now added a Table (Table 5), detailing the definition of each morphological feature that is extracted. On page 27, information on feature extraction is noted.

- Details that are missing or not sufficiently explained in the figure legends such as what each data point represents and what is Gini importance (Figure 1D) 

We have added these explanations to the figure legends. The Gini importance or mean decrease in impurity reflects how often this feature is used in decision tree splits across all random forest trees.

Is it the std shown in Figure 2C?

Yes, this has now been added to the legend.  

It is not fully clear what is single/mixed (Figure 2D)

Clarification is added to the legend and in the manuscript on page 6.

explain what is DIV 13-90 in the legend (Figure 5).

DIV stands for days in vitro, here it refers to the days in culture since the start of the neural induction process. This has been added in the legend.

and state what are img1-5 (Supplementary Figures 1B-C) Clarification has been added to the legend.

- Supplementary Figure 1. What is the y-axis in panel C and how do the results align with the cell mask in panel B?

The y-axis represents the intersection over union (IoU). The IoU quantifies the overlap between ground truth (manually segmented ROI) and the ROI detected by the segmentation algorithm. It is defined as the area of the overlapping region over the total area. This clarification has been added to the legend.

- Supplementary Figure 1 and Methods. Please explain when CellPose and when StarDist were applied.

Added to supplementary figure and methods at page 24. In the case of nuclear segmentation (nucleus and nucleocentric crops), Stardist was used. For whole-cell crops, cell segmentation using Cellpose was used.

- Supplementary Figure 4C - the color code is different between nuclear and nucleocentric - this is confusing.

We have changed to color code to correspond in both conditions in Fig. 1A.

- Figure 3B - better to have a normalized measure in the x-axis (number of cells per area in um^2)

We agree and have changed this.

Suggestions and missing/lacking details in the text:

• Line #38: "we then applied this" because it is the first time that this term is presented.

This has been rephrased.

• Line #88: a few words on what were the features extracted would be helpful.

Short description added to page 26-27 and detailed definition of all features added in table 5.

-  Line #91: PCA analysis - the authors can highlight what (known) features were important to PC1 using the linear transformation that defined it.

The 5 most important features of PC1 were (in order of decreasing importance): channel 1 dissimilarity, channel 1 homogeneity, nuclear perimeter, channel 4 dissimilarity and nuclear area.  

- Line #92: Order of referencing Supplementary Figure 4 before referencing Supplementary Figure 13.

The order of the Supplementary images was changed to follow the chronology. 

• Line #96: Can the authors show the data supporting this claim?

The unsupervised UMAP shown in fig. 1B is either color coded by cell type (left) or replicate (right). Based on this feature map, we observe clustering along the UMAP1 axis to be associated with the cell type. Variations in cellular morphology associated with the biological replicate are more visible along the UMAP2 axis. When looking at fig. 1C, the feature map reflecting the cellular area shows a gradient along the UMAP1 direction, supporting the assumption that cell area contributes to the cell type separation. On the other hand, the average intensity (Channel 2 intensity) has a gradient within the feature map along the UMAP2 direction. This corresponds to the pattern associated with the inter-replicate variability in panel B.

- Line #108: what is "nuclear Cy3 energy"?

This represents the local change of pixel intensities within the ROI in the nucleus in the 3rd channel dimension. This parameter reflects the texture within the nuclear region for the phalloidin and WGA staining. The definitions of all handcrafted features are added in table 5 of the manuscript.

- Line #110-112: Can the authors show the data supporting this claim?

The figure has been changed to include the results from a filtered and unfiltered dataframe (exclusion and inclusion of redundant features). Features could be filtered out if the correlation was above a threshold of 0.95. This has been added to page 6 of the manuscript and fig. 1D.  

- Line #115-116: please state the size of the mask.

Added to the text (page 6). We used isotropic image crops of 60µm centred on individual cell centroids.

- Lines 120-122: more details will make this more clear (single vs. mixed).

This has been changed on page 6 of the manuscript.

• Line #142: "(mimics)" - is it a typo?

Tissue mimics refers to organoids/models that are meant to replicate the physiological behaviour.

• Line #159: the bounding box for nucleocentric analysis is 15x15um (and not 60), as stated in the Methods.

Thank you for pointing out this mistake. We have adapted this.

- Line #165: what is the interpretation of what was important for the nucleocentric classification?

The colour code in GradCAM images is indicative of the attention of the CNN (the more to the red, the more attention). In fig. 4D and Suppl. Fig. 3 the structures directly surrounding the nucleus receive high attention from the CNN trained on nucleocentric crops. This has been added to the manuscript page 9 and 15.

• Section starting in line #172: not explicitly stated what model was used (nucleocentric?).

Added in the legend of fig. 5. For these experiments, the full cell segmentation was still used. 

- Section starting in line #199: why use a feature-based model rather than nucleocentric? A short sentence would be helpful.

For CNN training, nucleocentric profiling was used. In response to a legitimate question of one of the reviewers, the feature-based UMAP analysis was replaced with the feature embeddings from the CNN. 

- Line #213: Fig. 5B does not show transitioning cells.

Thank you for pointing this out, this was a mistake and has been changed.

Lines #218-220: not fully clear to some readers (culture condition as a weak label), more details can be helpful.

We changed this at page 11 of the manuscript for clarity. 

“This gating strategy resulted in a fractional abundance of neurons vs. total (neurons + NPC) of 36,4 % in the primed condition and 80,0% in the differentiated condition (Fig. 6C). We therefore refer to the culture condition as a weak label as it does not take into account the heterogeneity within each condition (well).”

-  Line #230: "increasing dendritic outgrowth" - what does it mean? Can you explicitly highlight this phenotype in Figure 5G?

When the cells become more mature during differentiation, the cell body becomes smaller and the neurons form long, thin ramifications. This explanation has been added to page 12 of the manuscript.

• Line #243: is it the nucleocentric CNN?

Yes.

• Lines #304-313, the authors might want to discuss other papers dealing with continuous (non-neural) differentiation state transitions (eg PMID: 38238594).  

A discussion of the use of morphological profiling for longitudinal follow-up of continuous differentiation states has been added to the manuscript at page 18. 

- Line #444: cellpose or stardist? How did the authors use both?

Clarification has been added to supplementary figure 1 and methods at page 24. Stardist was used for nuclear segmentation, whereas Cellpose was used for whole-cell segmentation. 

• Line #470-474: I would appreciate seeing the performance on the full dataset without exclusions.

Cells have been excluded based on 3 arguments: the absence of DAPI intensity, too small nuclear size and absence of ground truth staining. The first two arguments are based on the assumption that ROIs that contain no DAPI signal or are too small are errors in cell segmentation and therefore should not be taken along in the analysis. The third filtering step was based on the ground-truth IF signal. Not filtering out these cells with a ‘dubious’ IF profile (e.g., cells that might be transitioning or are of a different type) would negatively affect the model by introducing noise. It is correct that the predictions are based only on these inputs and so cells of a subsequent test set will only be classified according to these labels which might introduce bias. However, the model could predict increase in neuron/NPC ratio with culture age in absence of ground-truth staining (and thus IF-based filtering).

Reviewer #2 (Recommendations For The Authors):

Figure 1A: it would be interesting to the reader to see the SH-SY5Y data as well.

This has been added in fig. 1A.

Figure 3A: 95-100% image: showing images with the same magnification as the others would help to appreciate the cell density.

Now fig. 4A. The figure has been changed to make sure all images have the same magnification. 

Figure Supp 4 (line 132) is referred to before Figure Supp1 (line 152).

The image order and numbering has been changed to solve this issue.

Figure Supp 2 & 3 are not referred to in the text.

This has been adjusted.

Line 225: a statistical test would help to convince of the accuracy of these results (Figure 5C vs Figure 5F)?

These figures represent the total ROI counts and thus represent a single number.

Line 227: Could you explain to the reader, in a few words, what a dual SMAD inhibition is?

This has been added to the manuscript at page 20. 

“This dual blockade of SMAD signalling in iPSCs is induces neural differentiation by synergistically causing the loss of pluripotency and push towards neuroectodermal lineage.”

Reviewer #3 (Recommendations For The Authors):

I have a few concerns and several comments that, if addressed, may strengthen conclusions, and increase clarity of an already technically sound paper.

Concerns

• The results presented in Figure 3 panel D, may indicate a critical error in data processing and interpretation that the authors must address. The GradCAM method highlights the background as having the highest importance. While it can be argued in the nucleocentric profiling method that GradCAM focuses on the nuclear membrane, the background is highly important even for the nuclear profiling method, which should provide little information. What procedure did the authors use for mask subtraction prior to CNN training? Could the segmentation algorithm be performing differently between cell lines? The authors interpret the GradCAM results to indicate a proxy for nuclear size, but then why did the CNN perform so much better than random forest using hand-crafted features that include this variable? The authors should also present size distributions between cell lines (and across seeding densities, in case one of the cell lines has different compaction properties with increasing density).

Perhaps clarifying this sentence (lines 166-168) would help as well: "As nuclear area dropped with culture density, the dynamic range decreased, which could explain the increased error rate of the CNN for high densities unrelated to segmentation errors (Suppl. Fig. 4B)." What do the authors mean by "dynamic range" and it is not clear how Supplementary Figure 4B provides evidence for this? 

The dynamic range refers to the difference between the minimum and maximum nuclear area. We expect the difference to decrease at highe rdensity owing to the crowding that forces all nuclei to take on a more similar (smaller) size.

More clarification on this has been added to page 9 of the manuscript.

I certainly understand that extrapolating the GradCAM concern to the remaining single-cell images using only four (out of tens of thousands of options) is also dangerous, but so is "cherry-picking" these cells to visualize. Finally, I also recommend that the authors quantitatively diagnose the extent of the background influence according to GradCAM by systematically measuring background influence in all cells and displaying the results per cell line per density.

To avoid cherry picking of GradCAM images, we have now randomly selected for each condition and density 10 images (using random seeds to avoid cherry-picking) and added these in a Suppl. Fig. 3.

In answer to this concern, we refer to the response above: 

“To address the first point, we have adapted the GradCAM images to show an overlay of the input crop and GradCAM heatmap to give a better view of the structures that are highlighted by the CNN. We further investigated the influence of the background on the prediction performance. Our finding that a CNN trained on a monoculture retains a relatively high performance on cocultures implies that the CNN uses the salient characteristics of a cell to recognize it in more complex heterogeneous environments. Assuming that the background can vary between experiments, the prediction of a pretrained CNN on a new dataset indicates that cellular characteristics are used for robust prediction.  When inspecting GradCAM images obtained from the nucleocentric CNN approaches (now added in Suppl. Fig. 3), we noticed that the nuclear periphery typically contributed the most (but not exclusively) to the prediction performance. When using only the nuclear region as input, GradCAMs were more strongly (but again not exclusively) directed to the background surrounding the nuclei. To train the latter CNN, we had cropped nuclei and set the background to a value of zero. To rule out that this could have introduced a bias, we have now performed the exact same training and classification, but setting the background to random noise instead (Suppl. Fig. 2). While this effectively diverted the attention of the GradCAM output to the nucleus instead of the background, the prediction performance was unaltered. We therefore assume that irrespective of the background, when using nuclear crops as input, the CNN is dominated by features that describe nuclear size. We observe that nuclear size is significantly different in both cell types (although intranuclear features also still contribute) which is also reflected in the feature map gradient in the first UMAP dimension (Suppl. Fig. 2). This notion has been added to the manuscript (page 9) and Suppl. Fig. 2.”

• The data supporting the conclusion about nucleocentric profiling outperforming nuclear and full-cell profiling is minimal. I am picking on this conclusion in particular, because I think it is a super cool and elegant result that may change how folks approach issues stemming from cell density disproportionately impacting profiling. Figures 3B and 3C show nucleocentric slightly outperforming full cell, and the result is not significant. The authors state in lines 168-170: "Thus, we conclude that using the nucleocentric region as input for the CNN is a valuable strategy for accurate cell phenotype identification in dense cultures." This is somewhat of a weak conclusion, that, with additional analysis, could be strengthened and add high value to the community. Additionally, the authors describe the nucleocentric approach insufficiently. In the methods, the authors state (lines 501-503): "Cell crops (60μm whole cell - 15μm nucleocentric/nuclear area) were defined based on the segmentation mask for each ROI." This is not sufficient to reproduce the method. What software did the authors use?

Presumably, 60μm refers to a box size around cytoplasm? Much more detail is needed. Additionally, I suggest an analysis to confirm the impact of nucleocentric profiling, which would strengthen the authors' conclusions. I recommend systematically varying the subtraction (-30μm, -20μm, -10μm, 5μm, 0, +5μm, +10μm, etc.) and reporting the density-based analysis in Figure 3B per subtraction. I would expect to see some nucleocentric "sweet spot" where performance spikes, especially in high culture density. If we don't see this difference, then the non-significant result presented in Figures 3B and C is likely due to random chance. The authors mention "iterative data erosion" in the abstract, which might refer to what I am recommending, but do not describe this later.

More detail was added to the methods describing the image crops given as input to the CNN (page 28 of the manuscript). 

“Crops were defined based on the segmentation mask for each ROI. The bounding box was cropped out of the original image with a fixed patch size (60µm for whole cells, 18µm for nucleus and nucleocentric crops) surrounding the centroid of the segmentation mask. For the whole cell and nuclear crops, all pixels outside of the segmentation mask were set to zero. This was not the case for the nucleocentric crops. Each ROI was cropped out of the original morphological image and associated with metadata corresponding to its ground truth label.”

To address this concern, we also refer to the answer above. 

“We have performed a more extensive analysis in which the patch size was varied from 0.6 to 120µm around the nuclear centroid (Fig. 4E and page 9 of the manuscript). We observed that there is little effect of in- or decreasing patch size on the average F-score within the nuclear to cell window, but that the imbalance between the precision and recall increases towards the larger box sizes (>18µm). Under our experimental conditions, the input numbers per class were equal, but this will not be the case in situations where the ground truth is unknown (and needs to be predicted by the CNN). Therefore, a well-balanced CNN is of high importance. This notion has been added to page 12 of the manuscript.

The main advantage of nucleocentric profiling over whole-cell profiling in dense cultures is that it relies on a more robust nuclear segmentation method and is less sensitive to differences in cell density (Suppl. Fig. 1D). In other words, in dense cultures, the segmentation mask will contain similar regional input as the nuclear mask and the nucleocentric crop will contain more perinuclear information which contributes to the prediction accuracy. Therefore, at high densities, the performance of the CNN on whole-cell crops decreases owing to poorer segmentation performance. A CNN that uses nucleocentric crops, will be less sensitive to these errors. This notion has been added to pages 14-15 of the manuscript.“

Comments

• There is a disconnect between the abstract and the introduction. The abstract highlights the nucleocentric model, but then it is not discussed in the introduction, which focuses on quality control. The introduction would benefit from some additional description of the single-cell or whole-image approach to profiling.

We highlight the importance of QC of complex iPSC-derived neural cultures as an application of morphological profiling. We used single-cell profiling to facilitate cell identification in these mixed cultures where the whole-image approach would be unable to deal with the heterogeneity withing the field of view. In the introduction, we added a description of the whole-image vs. single-cell approach to profiling (page 4). In the discussion (page 18), we further highlight the application of this single-cell profiling approach for QC purposes. 

- Comments on Figure 1. It is unclear how panel B shows "without replicate bias". 

In response to this comment, we refer to the answer above: “The unsupervised UMAP shown in fig. 1B is either color coded by cell type (left) or replicate (right). Based on this feature map, we observe clustering along the UMAP1 axis to be associated with the cell type. Variations in cellular morphology associated with the biological replicate are more visible along the UMAP2 axis. When looking at fig. 1C, the feature map reflecting the cellular area shows a gradient along the UMAP1 direction, supporting the assumption that cell area contributes to the cell type separation. On the other hand, the average intensity (Channel 2 intensity) has a gradient within the feature map along the UMAP2 direction. This corresponds to the pattern associated with the inter-replicate variability in panel B.” We added this notion to page 5 of the manuscript.

The paper would benefit from a description of how features were extracted sooner.

Information on the feature extraction was added to the manuscript at page 27. An additional table (table 5) has been added with the definition of each feature.  

- Comments on Supplementary Figure 4. The clustering with PCA is only showing 2 dimensions, so it is not surprising UMAP shows more distinct clustering.

We used two components for UMAP dimensionality reduction, so the data was also visualized in two dimensions. However, we agree that UMAP can show more distinct clustering as this method is non-linear.

Why is Figure S4 the first referenced Supplementary Figure?

This has been changed. 

• Comments on Figure 2. Need discussion of the validation set - how was it determined? Panel E might have the answer I am looking for, but it is difficult to decipher exactly what is being done. The terminology needs to be defined somewhere, or maybe it is inconsistent. It is tough to tell. For example, what exactly are the two categories of model validation (cross-validation and independent testing)?

Additional clarification has been added to the manuscript at pages 6-7 and figure 2.

The metric being reported is accuracy for the independent replicate if the other two are used to train?

Yes. 

Panel C is a very cool analysis. Panel F needs a description of how those images were selected, randomly?

Added in the methods section (page 29). GradCAM analysis was used to visualize the regions used by the CNN for classification. This map is specific to each cell. Images are selected randomly out the full dataset for visualization.  

They also need scale bars.

Added to the figures. 

Panel G would benefit from explicit channel labels (at least a legend would be good!).

Explanation has been added to the legend. All color code and channel numbering are consistent with fig. 1A. 

What do the dots and boxplots represent? The legend says, "independent replicates", but independent replicates of, I assume, different model initializations?

Clarification has been added to the figure legends. For plots showing the performance of a CNN or RF classifier, each dot represents a different model initialization. Each classifier has been initialized at least 3 times. When indicated, the model training was performed with different random seeds for data splitting.

• Comments on Figure 3. Panel A needs scale bar. See comment on Panel D in concern #1 described above. 

This has been added.

• Comments on Supplementary Figure 1. A reader will need a more detailed description in panel C. I assume that the grey bar is the average of the points, and the points represent different single cells?

How many cells? How were these cells selected? 

This information on the figure (now Suppl. Fig. 1D), has been added to the legend.

“Left: Representative images of 1321N1 cells with increasing density alongside their cell and nuclear mask produced using resp. Cellpose and Stardist. Images are numbered from 1-5 with increasing density. Upper right: The number of ROIs detected in comparison to the ground truth (manual segmentation). A ROI was considered undetected when the intersection over union (IoU) was below 0,15. Each bar refers to the image number on the left. The IoU quantifies the overlap between ground truth (manually segmented ROI) and the ROI detected by the segmentation algorithm. It is defined as the area of the overlapping region over the total area. IoU for increasing cell density for cell and nuclear masks is given in the bottom right. Each point represents an individual ROI. Each bar refers to the image number on the left.”

• Comments on Figure 4. More details on quenching are needed for a general audience. The markers chosen (EdU and BrdU) are generally not specific to cell type but to biological processes (proliferation), so it is confusing how they are being used as cell-type markers. 

The base analogues were incorporated into each cell line prior to mixing them, i.e.  when they were still growing in monoculture so they could be labelled and identified after co-seeding and morphological profiling. Additional clarification has been added to the manuscript (page 26) 

It is also unclear why reducing CV is an important side-effect of finetuning. CV of what? The legend says, "model iterations", but what does this mean? 

The dots in the violinplot are different CNN initializations. A lower variability between model initializations is an indicator of certainty of the results. Prior to finetuning, the results of the CNN were highly variable leading to a high CoV between the different CNNs. This means the outcome after finetuning is more robust.

• Comments on Figure 5. This is a very convincing and well-described result, kudos! This provides another opportunity to again compare other approaches (not just nucleocentric). Additionally, since the UMAP space uses hand-crafted features. The authors could consider interpreting the specific morphology features impacted by the striking gradual shift to neuron population by fitting a series of linear models per individual feature. This might confirm (or discover) how exactly the cells are shifting morphology.

The supervised UMAP on the handcrafted features did not highlight any features contributing to the separation. Using the supervised UMAP, the clustering is dominated by the known cell type. Unsupervised UMAP on the handcrafted features does not show any clustering. In response to a previous comment, we adapted the figure to show UMAP dimensionality reduction using the feature embeddings from the cell-based CNN. This unsupervised UMAP does show good cell type separation, but it does not use any directly interpretable shape descriptors.

• General comments on Methods. The section on "ground truth alignment" needs more details. Why was this performed? 

Following sequential staining and imaging rounds, multiple images were captured representing the same cell with different markers. Lifting the plate of the microscope stage and imaging in sequential rounds after several days results in small linear translations in the exact location of each image. These linear translations need to be corrected to align (or register) morphological with ground truth image data within the same ROI. This notion has been added to the manuscript at page 26. 

Handcrafted features extracted using what software? 

The complete analysis was performed in python. All packages used are listed in table 4. Handcrafted features were extracted using the scikit-image package (regionprops and GLCM functions). This has been added to the manuscript at page 27.

Software should be cited more often throughout the manuscript. 

Lastly, the GitHub URL points to the DeVosLab organization, but should point to a specific repository. Therefore, I was unable to review the provided code. A well-documented and reproducible analysis pipeline should be included.

A test dataset and source code are available on GitHub:  https://github.com/DeVosLab/Nucleocentric-Profiling

Is there a sweet spot between these two methods?

Reviewer #1 (Public Review):

Summary:

Glaser et al present ExA-SPIM, a light-sheet microscope platform with large volumetric coverage (Field of view 85mm^2, working distance 35mm ), designed to image expanded mouse brains in their entirety. The authors also present an expansion method optimized for whole mouse brains, and an acquisition software suite. The microscope is employed in imaging an expanded mouse brain, the macaque motor cortex and human brain slices of white matter.
This is impressive work, and represents a leap over existing light-sheet microscopes. As an example, it offers a ~ fivefold higher resolution than mesoSPIM (https://mesospim.org/), a popular platform for imaging large cleared samples. Thus while this work is rooted in optical engineering, it manifests a huge step forward and has the potential to become an important tool in the neurosciences.

Strengths:

-ExA-SPIM features an exceptional combination of field of view, working distance, resolution and throughput.

-An expanded mouse brain can be acquired with only 15 tiles, lowering the burden on computational stitching. That the brain does not need to be mechanically sectioned is also seen as an important capability.

-The image data is compelling, and tracing of neurons has been performed. This demonstrates the potential of the microscope platform.

Weaknesses:

-There is a general question about the scaling laws of lenses, and expansion microscopy, which in my opinion remained unanswered: In the context of whole brain imaging, a larger expansion factor requires a microscope system with larger volumetric coverage, which in turn will have lower resolution (Figure 1B). So what is optimal? Could one alternatively image a cleared (non-expanded) brain with a high resolution ASLM system (Chakraborty, Tonmoy, Nature Methods 2019, potentially upgraded with custom objectives) and get similar effective resolution as the authors get with expansion? This is not meant to diminish the achievement, but it was unclear if the gains in resolution from the expansion factor are traded off by the scaling laws of current optical systems.

-It was unclear if 300 nm lateral and 800 nm axial resolution is enough for many questions in neuroscience. Segmenting spines, distinguishing pre- and postsynaptic densities, or tracing densely labeled neurons might be challenging. A discussion about the necessary resolution levels in neuroscience would be appreciated.

-Would it be possible to characterize the aberrations that might be still present after whole brain expansion? One approach could be to image small fluorescent nanospheres behind the expanded brain, and recover the pupil function via phase retrieval. But even full width half maximum (FWHM) measurements of the nanospheres' images would give some idea of the magnitude of the aberrations.

Review of the revised manuscript:

The authors have carefully addressed my concerns and suggestions.

I appreciate the extended discussion on tissue clearing compared to expansion. I would recommend substantiating some of the statements though with references, or in other instances expanding a little further. I would encourage the authors to consider the points below. But there is also another path to actually reduce that specific discussion, if the conclusion is that it opened more questions than answers.

Specifically, here are some points in the paragraph that discusses tissue clearing and expansion that could be improved:
-The statement "Spherical aberration increases with NA" reads nonspecific to me. I think a more precise formulation would be "The effect of spherical aberration (e.g. loss of Strehl ratio) increases with NA. The stated third power law would also benefit from a reference.
-The statement "the index of refraction gradients in tissue decreases with the third power of the expansion factor..." reads a bit odd. "Gradients in refractive index" would be more consistent with the usage of r.i. throughout the manuscript.
For the third power law, it might be important to know what drives the remaining refractive index variation in expansion microscopy. If it is the labels and their linkers, then indeed, they get increasingly diluted as their amount remains constant. However, if the aberrations are caused by the polymer gel, I would assume you would need more monomer material for higher expansion factors? Thus, I was not fully sure about the scaling law in this case. If there is a reference where this was explored in detail, that would resolve this issue.

-The statement that aberrations scale with gradients in refractive index also needs either a reference, or an explanation for the reader. I think figure S4 was supposed to illustrate this, but was not referenced in the discussion (and could be clarified, see comment below).

To me, the discussion focused strongly on tissue clearing vs expansion. What was left out in the discussion was if larger expansion factors would be favorable (i.e. whole brain imaging with 10-20X expansion instead of 4-5X). Some arguments implicitly seemed to stipulate that a larger expansion factor would optically be favorable. But Figure S7 highlights another tradeoff with the decay in sensitivity and Figure 1b provides the technological constraints on lens design. So as a reader, I was not fully sure if the next frontier should be 10-20X expansion brain imaging, or if 4-5X is currently a sweet spot.

Further comments:

Please explain the variables in Figure S4, such as F, WD and d. It was unclear to me what the RI profile should mean in the bottom row. Naively, the figure of merit would be the optical path length that is integrated along the different rays, as this leads to a variation in the wavefront.

Figure S5: I would caution to say the SNR was quantified, but rather say it was estimated (in the shot noise limit). Was the background subtracted for the SNR measurements?
Squaring the SNR estimates, it looks like the photon counts went down ~10-fold from z=2mm to z=25mm. That is a larger reduction in signal than I had expected. If it was based solely on aberrations, a 10-fold drop in Strehl ratio seems significant (potentially smaller if we assume the light-sheet also underwent aberrations). Are there other factors that could explain the signal reduction (maybe from the labeling side)?
Further on Figure S5: Fourier transforms (power spectrum) and single line profiles are in my opinion not the best way to quantify resolution. Could the authors perform image decorrelation analysis on the region of interest (Descloux, A., Kristin Stefanie Grußmayer, and Aleksandra Radenovic. "Parameter-free image resolution estimation based on decorrelation analysis." Nature methods 2019) or Fourier ring correlation? This would give in some sense an average resolving power in that depth, and would remove the bias from picking a line profile.

Reviewer #3 (Public Review):

Summary:
Bos et al study a computational model of cortical circuits with excitatory (E) and two subtypes of inhibition - parvalbumin (PV) and somatostatin (SOM) expressing interneurons. They perform stability and gain analysis of simplified models with nonlinear transfer functions when SOM neurons are perturbed. Their analysis suggests that in a specific setup of connectivity, instability and gain can be untangled, such that SOM modulation leads to both increases in stability and gain. This is in contrast with the typical direction in neuronal networks where increased gain results in decreased stability.

Strengths:
- Analysis of the canonical circuit in response to SOM perturbations. Through numerical simulations and mathematical analysis, the authors have provided a rather comprehensive picture of how SOM modulation may affect response changes.

- Shedding light on two opposing circuit motifs involved in the canonical E-PV-SOM circuitry - namely, direct inhibition (SOM -> E) vs disinhibition (SOM -> PV -> E). These two pathways can lead to opposing effects, and it is often difficult to predict which one results from modulating SOM neurons. In simplified circuits, the authors show how these two motifs can emerge and depend on parameters like connection weights.

- Suggesting potentially interesting consequences for cortical computation. The authors suggest that certain regimes of connectivity may lead to untangling of stability and gain, such that increases in network gain are not compromised by decreasing stability. They also link SOM modulation in different connectivity regimes to versatile computations in visual processing in simple models.

Weaknesses:
The computational analysis is not novel per se, and the link to biology is not direct/clear.

Computationally, the analysis is solid, but it's very similar to previous studies (del Molino et al, 2017). Many studies in the past few years have done the perturbation analysis of a similar circuitry with or without nonlinear transfer functions (some of them listed in the references). This study applies the same framework to SOM perturbations, which is a useful and interesting computational exercise, in view of the complexity of the high-dimensional parameter space. But the mathematical framework is not novel per se, undermining the claim of providing a new framework (or "circuit theory").

Link to biology: the most interesting result of the paper with regard to biology is the suggestion of a regime in which gain and stability can be modulated in an unconventional way - however, it is difficult to link the results to biological networks:
- A general weakness of the paper is a lack of direct comparison to biological parameters or experiments. How different experiments can be reconciled by the results obtained here, and what new circuit mechanisms can be revealed? In its current form, the paper reads as a general suggestion that different combinations of gain modulation and stability can be achieved in a circuit model equipped with many parameters (12 parameters). This is potentially interesting but not surprising, given the high dimensional space of possible dynamical properties. A more interesting result would have been to relate this to biology, by providing reasoning why it might be relevant to certain circuits (and not others), or to provide some predictions or postdictions, which are currently missing in the manuscript.
- For instance, a nice motivation for the paper at the beginning of the Results section is the different results of SOM modulation in different experiments - especially between L23 (inhibition) and L4 (disinhibition). But no further explanation is provided for why such a difference should exist, in view of their results and the insights obtained from their suggested circuit mechanisms. How the parameters identified for the two regimes correspond to different properties of different layers?
- Another caveat is the range of parameters needed to obtain the unintuitive untangling as a result of SOM modulation. From Figure 4, it appears that the "interesting" regime (with increases in both gain and stability) is only feasible for a very narrow range of SOM firing rates (before 3 Hz). This can be a problem for the computational models if the sweet spot is a very narrow region (this analysis is by the way missing, so making it difficult to know how robust the result is in terms of parameter regions). In terms of biology, it is difficult to reconcile this with the realistic firing rates in the cortex: in the mouse cortex, for instance, we know that SOM neurons can be quite active (comparable to E neurons), especially in response to stimuli. It is therefore not clear if we should expect this mechanism to be a relevant one for cortical activity regimes.
- One of the key assumptions of the model is nonlinear transfer functions for all neuron types. In terms of modelling and computational analysis, a thorough analysis of how and when this is necessary is missing (an analysis similar to what has been attempted at in Figure 6 for synaptic weights, but for cellular gains). In terms of biology, the nonlinear transfer function has experimentally been reported for excitatory neurons, so it's not clear to what extent this may hold for different inhibitory subtypes. A discussion of this, along with the former analysis to know which nonlinearities would be necessary for the results, is needed, but currently missing from the study. The nonlinearity is assumed for all subtypes because it seems to be needed to obtain the results, but it's not clear how the model would behave in the presence or absence of them, and whether they are relevant to biological networks with inhibitory transfer functions.
- Tuning curves are simulated for an individual orientation (same for all), not considering the heterogeneity of neuronal networks with multiple orientation selectivity (and other visual features) - making the model too simplistic.

Does it? I wonder if that is true, given your past work with the flight I would have thought fine it unlikely that both match. Though in the end you know best.

According to all known laws of aviation,

there is no way a bee should be able to fly.

Its wings are too small to get its fat little body off the ground.

The bee, of course, flies anyway

because bees don't care what humans think is impossible.

Yellow, black. Yellow, black. Yellow, black. Yellow, black.

Ooh, black and yellow! Let's shake it up a little.

Barry! Breakfast is ready!

Ooming!

Hang on a second.

Hello?

Barry?

Adam?

Oan you believe this is happening?

I can't. I'll pick you up.

Looking sharp.

Use the stairs. Your father paid good money for those.

Sorry. I'm excited.

Here's the graduate. We're very proud of you, son.

A perfect report card, all B's.

Very proud.

Ma! I got a thing going here.

You got lint on your fuzz.

Ow! That's me!

Wave to us! We'll be in row 118,000.

Bye!

Barry, I told you, stop flying in the house!

Hey, Adam.

Hey, Barry.

Is that fuzz gel?

A little. Special day, graduation.

Never thought I'd make it.

Three days grade school, three days high school.

Those were awkward.

Three days college. I'm glad I took a day and hitchhiked around the hive.

You did come back different.

Hi, Barry.

Artie, growing a mustache? Looks good.

Hear about Frankie?

Yeah.

You going to the funeral?

No, I'm not going.

Everybody knows, sting someone, you die.

Don't waste it on a squirrel. Such a hothead.

I guess he could have just gotten out of the way.

I love this incorporating an amusement park into our day.

That's why we don't need vacations.

Boy, quite a bit of pomp… under the circumstances.

Well, Adam, today we are men.

We are!

Bee-men.

Amen!

Hallelujah!

Students, faculty, distinguished bees,

please welcome Dean Buzzwell.

Welcome, New Hive Oity graduating class of…

…9:15.

That concludes our ceremonies.

And begins your career at Honex Industries!

Will we pick ourjob today?

I heard it's just orientation.

Heads up! Here we go.

Keep your hands and antennas inside the tram at all times.

Wonder what it'll be like? A little scary. Welcome to Honex, a division of Honesco

and a part of the Hexagon Group.

This is it!

Wow.

Wow.

We know that you, as a bee, have worked your whole life

to get to the point where you can work for your whole life.

Honey begins when our valiant Pollen Jocks bring the nectar to the hive.

Our top-secret formula

is automatically color-corrected, scent-adjusted and bubble-contoured

into this soothing sweet syrup

with its distinctive golden glow you know as…

Honey!

That girl was hot.

She's my cousin!

She is?

Yes, we're all cousins.

Right. You're right.

At Honex, we constantly strive

to improve every aspect of bee existence.

These bees are stress-testing a new helmet technology.

What do you think he makes? Not enough. Here we have our latest advancement, the Krelman.

What does that do? Oatches that little strand of honey that hangs after you pour it. Saves us millions.

Oan anyone work on the Krelman?

Of course. Most bee jobs are small ones. But bees know

that every small job, if it's done well, means a lot.

But choose carefully

because you'll stay in the job you pick for the rest of your life.

The same job the rest of your life? I didn't know that.

What's the difference?

You'll be happy to know that bees, as a species, haven't had one day off

in 27 million years.

So you'll just work us to death?

We'll sure try.

Wow! That blew my mind!

"What's the difference?" How can you say that?

One job forever? That's an insane choice to have to make.

I'm relieved. Now we only have to make one decision in life.

But, Adam, how could they never have told us that?

Why would you question anything? We're bees.

We're the most perfectly functioning society on Earth.

You ever think maybe things work a little too well here?

Like what? Give me one example.

I don't know. But you know what I'm talking about.

Please clear the gate. Royal Nectar Force on approach.

Wait a second. Oheck it out.

Hey, those are Pollen Jocks! Wow. I've never seen them this close.

They know what it's like outside the hive.

Yeah, but some don't come back.

Hey, Jocks! Hi, Jocks! You guys did great!

You're monsters! You're sky freaks! I love it! I love it!

I wonder where they were. I don't know. Their day's not planned.

Outside the hive, flying who knows where, doing who knows what.

You can'tjust decide to be a Pollen Jock. You have to be bred for that.

Right.

Look. That's more pollen than you and I will see in a lifetime.

It's just a status symbol. Bees make too much of it.

Perhaps. Unless you're wearing it and the ladies see you wearing it.

Those ladies? Aren't they our cousins too?

Distant. Distant.

Look at these two.

Oouple of Hive Harrys. Let's have fun with them. It must be dangerous being a Pollen Jock.

Yeah. Once a bear pinned me against a mushroom!

He had a paw on my throat, and with the other, he was slapping me!

Oh, my! I never thought I'd knock him out. What were you doing during this?

Trying to alert the authorities.

I can autograph that.

A little gusty out there today, wasn't it, comrades?

Yeah. Gusty.

We're hitting a sunflower patch six miles from here tomorrow.

Six miles, huh? Barry! A puddle jump for us, but maybe you're not up for it.

Maybe I am. You are not! We're going 0900 at J-Gate.

What do you think, buzzy-boy? Are you bee enough?

I might be. It all depends on what 0900 means.

Hey, Honex!

Dad, you surprised me.

You decide what you're interested in?

Well, there's a lot of choices. But you only get one. Do you ever get bored doing the same job every day?

Son, let me tell you about stirring.

You grab that stick, and you just move it around, and you stir it around.

You get yourself into a rhythm. It's a beautiful thing.

You know, Dad, the more I think about it,

maybe the honey field just isn't right for me.

You were thinking of what, making balloon animals?

That's a bad job for a guy with a stinger.

Janet, your son's not sure he wants to go into honey!

Barry, you are so funny sometimes. I'm not trying to be funny. You're not funny! You're going into honey. Our son, the stirrer!

You're gonna be a stirrer? No one's listening to me! Wait till you see the sticks I have.

I could say anything right now. I'm gonna get an ant tattoo!

Let's open some honey and celebrate!

Maybe I'll pierce my thorax. Shave my antennae.

Shack up with a grasshopper. Get a gold tooth and call everybody "dawg"!

I'm so proud.

We're starting work today! Today's the day. Oome on! All the good jobs will be gone.

Yeah, right.

Pollen counting, stunt bee, pouring, stirrer, front desk, hair removal…

Is it still available? Hang on. Two left! One of them's yours! Oongratulations! Step to the side.

What'd you get? Picking crud out. Stellar! Wow!

Oouple of newbies?

Yes, sir! Our first day! We are ready!

Make your choice.

You want to go first? No, you go. Oh, my. What's available?

Restroom attendant's open, not for the reason you think.

Any chance of getting the Krelman? Sure, you're on. I'm sorry, the Krelman just closed out.

Wax monkey's always open.

The Krelman opened up again.

What happened?

A bee died. Makes an opening. See? He's dead. Another dead one.

Deady. Deadified. Two more dead.

Dead from the neck up. Dead from the neck down. That's life!

Oh, this is so hard!

Heating, cooling, stunt bee, pourer, stirrer,

humming, inspector number seven, lint coordinator, stripe supervisor,

mite wrangler. Barry, what do you think I should… Barry?

Barry!

All right, we've got the sunflower patch in quadrant nine…

What happened to you? Where are you?

I'm going out.

Out? Out where?

Out there.

Oh, no!

I have to, before I go to work for the rest of my life.

You're gonna die! You're crazy! Hello?

Another call coming in.

If anyone's feeling brave, there's a Korean deli on 83rd

that gets their roses today.

Hey, guys.

Look at that. Isn't that the kid we saw yesterday? Hold it, son, flight deck's restricted.

It's OK, Lou. We're gonna take him up.

Really? Feeling lucky, are you?

Sign here, here. Just initial that.

Thank you. OK. You got a rain advisory today,

and as you all know, bees cannot fly in rain.

So be careful. As always, watch your brooms,

hockey sticks, dogs, birds, bears and bats.

Also, I got a couple of reports of root beer being poured on us.

Murphy's in a home because of it, babbling like a cicada!

That's awful. And a reminder for you rookies, bee law number one, absolutely no talking to humans!

All right, launch positions!

Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz!

Black and yellow!

Hello!

You ready for this, hot shot?

Yeah. Yeah, bring it on.

Wind, check.

Antennae, check.

Nectar pack, check.

Wings, check.

Stinger, check.

Scared out of my shorts, check.

OK, ladies,

let's move it out!

Pound those petunias, you striped stem-suckers!

All of you, drain those flowers!

Wow! I'm out!

I can't believe I'm out!

So blue.

I feel so fast and free!

Box kite!

Wow!

Flowers!

This is Blue Leader. We have roses visual.

Bring it around 30 degrees and hold.

Roses!

30 degrees, roger. Bringing it around.

Stand to the side, kid. It's got a bit of a kick.

That is one nectar collector!

Ever see pollination up close? No, sir. I pick up some pollen here, sprinkle it over here. Maybe a dash over there,

a pinch on that one. See that? It's a little bit of magic.

That's amazing. Why do we do that?

That's pollen power. More pollen, more flowers, more nectar, more honey for us.

Oool.

I'm picking up a lot of bright yellow. Oould be daisies. Don't we need those?

Oopy that visual.

Wait. One of these flowers seems to be on the move.

Say again? You're reporting a moving flower?

Affirmative.

That was on the line!

This is the coolest. What is it?

I don't know, but I'm loving this color.

It smells good. Not like a flower, but I like it.

Yeah, fuzzy.

Ohemical-y.

Oareful, guys. It's a little grabby.

My sweet lord of bees!

Oandy-brain, get off there!

Problem!

Guys! This could be bad. Affirmative.

Very close.

Gonna hurt.

Mama's little boy.

You are way out of position, rookie!

Ooming in at you like a missile!

Help me!

I don't think these are flowers.

Should we tell him? I think he knows. What is this?!

Match point!

You can start packing up, honey, because you're about to eat it!

Yowser!

Gross.

There's a bee in the car!

Do something!

I'm driving!

Hi, bee.

He's back here!

He's going to sting me!

Nobody move. If you don't move, he won't sting you. Freeze!

He blinked!

Spray him, Granny!

What are you doing?!

Wow… the tension level out here is unbelievable.

I gotta get home.

Oan't fly in rain.

Oan't fly in rain.

Oan't fly in rain.

Mayday! Mayday! Bee going down!

Ken, could you close the window please?

Ken, could you close the window please?

Oheck out my new resume. I made it into a fold-out brochure.

You see? Folds out.

Oh, no. More humans. I don't need this.

What was that?

Maybe this time. This time. This time. This time! This time! This…

Drapes!

That is diabolical.

It's fantastic. It's got all my special skills, even my top-ten favorite movies.

What's number one? Star Wars?

Nah, I don't go for that…

…kind of stuff.

No wonder we shouldn't talk to them. They're out of their minds.

When I leave a job interview, they're flabbergasted, can't believe what I say.

There's the sun. Maybe that's a way out.

I don't remember the sun having a big 75 on it.

I predicted global warming.

I could feel it getting hotter. At first I thought it was just me.

Wait! Stop! Bee!

Stand back. These are winter boots.

Wait!

Don't kill him!

You know I'm allergic to them! This thing could kill me!

Why does his life have less value than yours?

Why does his life have any less value than mine? Is that your statement?

I'm just saying all life has value. You don't know what he's capable of feeling.

My brochure!

There you go, little guy.

I'm not scared of him. It's an allergic thing.

Put that on your resume brochure.

My whole face could puff up.

Make it one of your special skills.

Knocking someone out is also a special skill.

Right. Bye, Vanessa. Thanks.

Vanessa, next week? Yogurt night?

Sure, Ken. You know, whatever.

You could put carob chips on there.

Bye.

Supposed to be less calories.

Bye.

I gotta say something.

She saved my life. I gotta say something.

All right, here it goes.

Nah.

What would I say?

I could really get in trouble.

It's a bee law. You're not supposed to talk to a human.

I can't believe I'm doing this.

I've got to.

Oh, I can't do it. Oome on!

No. Yes. No.

Do it. I can't.

How should I start it? "You like jazz?" No, that's no good.

Here she comes! Speak, you fool!

Hi!

I'm sorry.

You're talking. Yes, I know. You're talking!

I'm so sorry.

No, it's OK. It's fine. I know I'm dreaming.

But I don't recall going to bed.

Well, I'm sure this is very disconcerting.

This is a bit of a surprise to me. I mean, you're a bee!

I am. And I'm not supposed to be doing this,

but they were all trying to kill me.

And if it wasn't for you…

I had to thank you. It's just how I was raised.

That was a little weird.

I'm talking with a bee. Yeah. I'm talking to a bee. And the bee is talking to me!

I just want to say I'm grateful. I'll leave now.

Wait! How did you learn to do that? What? The talking thing.

Same way you did, I guess. "Mama, Dada, honey." You pick it up.

That's very funny. Yeah. Bees are funny. If we didn't laugh, we'd cry with what we have to deal with.

Anyway…

Oan I…

…get you something?

Like what? I don't know. I mean… I don't know. Ooffee?

I don't want to put you out.

It's no trouble. It takes two minutes.

It's just coffee.

I hate to impose.

Don't be ridiculous!

Actually, I would love a cup.

Hey, you want rum cake?

I shouldn't.

Have some.

No, I can't.

Oome on!

I'm trying to lose a couple micrograms.

Where? These stripes don't help. You look great!

I don't know if you know anything about fashion.

Are you all right?

No.

He's making the tie in the cab as they're flying up Madison.

He finally gets there.

He runs up the steps into the church. The wedding is on.

And he says, "Watermelon? I thought you said Guatemalan.

Why would I marry a watermelon?"

Is that a bee joke?

That's the kind of stuff we do.

Yeah, different.

So, what are you gonna do, Barry?

About work? I don't know.

I want to do my part for the hive, but I can't do it the way they want.

I know how you feel.

You do? Sure. My parents wanted me to be a lawyer or a doctor, but I wanted to be a florist.

Really? My only interest is flowers. Our new queen was just elected with that same campaign slogan.

Anyway, if you look…

There's my hive right there. See it?

You're in Sheep Meadow!

Yes! I'm right off the Turtle Pond!

No way! I know that area. I lost a toe ring there once.

Why do girls put rings on their toes?

Why not?

It's like putting a hat on your knee.

Maybe I'll try that.

You all right, ma'am?

Oh, yeah. Fine.

Just having two cups of coffee!

Anyway, this has been great. Thanks for the coffee.

Yeah, it's no trouble.

Sorry I couldn't finish it. If I did, I'd be up the rest of my life.

Are you…?

Oan I take a piece of this with me?

Sure! Here, have a crumb.

Thanks! Yeah. All right. Well, then… I guess I'll see you around.

Or not.

OK, Barry.

And thank you so much again… for before.

Oh, that? That was nothing.

Well, not nothing, but… Anyway…

This can't possibly work.

He's all set to go. We may as well try it.

OK, Dave, pull the chute.

Sounds amazing. It was amazing! It was the scariest, happiest moment of my life.

Humans! I can't believe you were with humans!

Giant, scary humans! What were they like?

Huge and crazy. They talk crazy.

They eat crazy giant things. They drive crazy.

Do they try and kill you, like on TV?

Some of them. But some of them don't.

How'd you get back?

Poodle.

You did it, and I'm glad. You saw whatever you wanted to see.

You had your "experience." Now you can pick out yourjob and be normal.

Well… Well? Well, I met someone.

You did? Was she Bee-ish?

A wasp?! Your parents will kill you!

No, no, no, not a wasp.

Spider?

I'm not attracted to spiders.

I know it's the hottest thing, with the eight legs and all.

I can't get by that face.

So who is she?

She's… human.

No, no. That's a bee law. You wouldn't break a bee law.

Her name's Vanessa. Oh, boy. She's so nice. And she's a florist!

Oh, no! You're dating a human florist!

We're not dating.

You're flying outside the hive, talking to humans that attack our homes

with power washers and M-80s! One-eighth a stick of dynamite!

She saved my life! And she understands me.

This is over!

Eat this.

This is not over! What was that?

They call it a crumb. It was so stingin' stripey! And that's not what they eat. That's what falls off what they eat!

You know what a Oinnabon is? No. It's bread and cinnamon and frosting. They heat it up…

Sit down!

…really hot!

Listen to me! We are not them! We're us. There's us and there's them!

Yes, but who can deny the heart that is yearning?

There's no yearning. Stop yearning. Listen to me!

You have got to start thinking bee, my friend. Thinking bee!

Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee! Thinking bee!

There he is. He's in the pool.

You know what your problem is, Barry?

I gotta start thinking bee?

How much longer will this go on?

It's been three days! Why aren't you working?

I've got a lot of big life decisions to think about.

What life? You have no life! You have no job. You're barely a bee!

Would it kill you to make a little honey?

Barry, come out. Your father's talking to you.

Martin, would you talk to him?

Barry, I'm talking to you!

You coming?

Got everything?

All set!

Go ahead. I'll catch up.

Don't be too long.

Watch this!

Vanessa!

We're still here. I told you not to yell at him. He doesn't respond to yelling!

Then why yell at me? Because you don't listen! I'm not listening to this.

Sorry, I've gotta go.

Where are you going? I'm meeting a friend. A girl? Is this why you can't decide?

Bye.

I just hope she's Bee-ish.

They have a huge parade of flowers every year in Pasadena?

To be in the Tournament of Roses, that's every florist's dream!

Up on a float, surrounded by flowers, crowds cheering.

A tournament. Do the roses compete in athletic events?

No. All right, I've got one. How come you don't fly everywhere?

It's exhausting. Why don't you run everywhere? It's faster.

Yeah, OK, I see, I see. All right, your turn.

TiVo. You can just freeze live TV? That's insane!

You don't have that?

We have Hivo, but it's a disease. It's a horrible, horrible disease.

Oh, my.

Dumb bees!

You must want to sting all those jerks.

We try not to sting. It's usually fatal for us.

So you have to watch your temper.

Very carefully. You kick a wall, take a walk,

write an angry letter and throw it out. Work through it like any emotion:

Anger, jealousy, lust.

Oh, my goodness! Are you OK?

Yeah.

What is wrong with you?! It's a bug. He's not bothering anybody. Get out of here, you creep!

What was that? A Pic 'N' Save circular?

Yeah, it was. How did you know?

It felt like about 10 pages. Seventy-five is pretty much our limit.

You've really got that down to a science.

I lost a cousin to Italian Vogue. I'll bet. What in the name of Mighty Hercules is this?

How did this get here? Oute Bee, Golden Blossom,

Ray Liotta Private Select?

Is he that actor?

I never heard of him.

Why is this here?

For people. We eat it.

You don't have enough food of your own?

Well, yes.

How do you get it?

Bees make it.

I know who makes it!

And it's hard to make it!

There's heating, cooling, stirring. You need a whole Krelman thing!

It's organic. It's our-ganic! It's just honey, Barry.

Just what?!

Bees don't know about this! This is stealing! A lot of stealing!

You've taken our homes, schools, hospitals! This is all we have!

And it's on sale?! I'm getting to the bottom of this.

I'm getting to the bottom of all of this!

Hey, Hector.

You almost done? Almost. He is here. I sense it.

Well, I guess I'll go home now

and just leave this nice honey out, with no one around.

You're busted, box boy!

I knew I heard something. So you can talk!

I can talk. And now you'll start talking!

Where you getting the sweet stuff? Who's your supplier?

I don't understand. I thought we were friends.

The last thing we want to do is upset bees!

You're too late! It's ours now!

You, sir, have crossed the wrong sword!

You, sir, will be lunch for my iguana, Ignacio!

Where is the honey coming from?

Tell me where!

Honey Farms! It comes from Honey Farms!

Orazy person!

What horrible thing has happened here?

These faces, they never knew what hit them. And now

they're on the road to nowhere!

Just keep still.

What? You're not dead?

Do I look dead? They will wipe anything that moves. Where you headed?

To Honey Farms. I am onto something huge here.

I'm going to Alaska. Moose blood, crazy stuff. Blows your head off!

I'm going to Tacoma.

And you? He really is dead. All right.

Uh-oh!

What is that?!

Oh, no!

A wiper! Triple blade!

Triple blade?

Jump on! It's your only chance, bee!

Why does everything have to be so doggone clean?!

How much do you people need to see?!

Open your eyes! Stick your head out the window!

From NPR News in Washington, I'm Oarl Kasell.

But don't kill no more bugs!

Bee!

Moose blood guy!!

You hear something?

Like what?

Like tiny screaming.

Turn off the radio.

Whassup, bee boy?

Hey, Blood.

Just a row of honey jars, as far as the eye could see.

Wow!

I assume wherever this truck goes is where they're getting it.

I mean, that honey's ours.

Bees hang tight. We're all jammed in. It's a close community.

Not us, man. We on our own. Every mosquito on his own.

What if you get in trouble? You a mosquito, you in trouble. Nobody likes us. They just smack. See a mosquito, smack, smack!

At least you're out in the world. You must meet girls.

Mosquito girls try to trade up, get with a moth, dragonfly.

Mosquito girl don't want no mosquito.

You got to be kidding me!

Mooseblood's about to leave the building! So long, bee!

Hey, guys! Mooseblood! I knew I'd catch y'all down here. Did you bring your crazy straw?

We throw it in jars, slap a label on it, and it's pretty much pure profit.

What is this place?

A bee's got a brain the size of a pinhead.

They are pinheads!

Pinhead.

Oheck out the new smoker. Oh, sweet. That's the one you want. The Thomas 3000!

Smoker?

Ninety puffs a minute, semi-automatic. Twice the nicotine, all the tar.

A couple breaths of this knocks them right out.

They make the honey, and we make the money.

"They make the honey, and we make the money"?

Oh, my!

What's going on? Are you OK?

Yeah. It doesn't last too long.

Do you know you're in a fake hive with fake walls?

Our queen was moved here. We had no choice.

This is your queen? That's a man in women's clothes!

That's a drag queen!

What is this?

Oh, no!

There's hundreds of them!

Bee honey.

Our honey is being brazenly stolen on a massive scale!

This is worse than anything bears have done! I intend to do something.

Oh, Barry, stop.

Who told you humans are taking our honey? That's a rumor.

Do these look like rumors?

That's a conspiracy theory. These are obviously doctored photos.

How did you get mixed up in this?

He's been talking to humans.

What? Talking to humans?! He has a human girlfriend. And they make out!

Make out? Barry!

We do not.

You wish you could. Whose side are you on? The bees!

I dated a cricket once in San Antonio. Those crazy legs kept me up all night.

Barry, this is what you want to do with your life?

I want to do it for all our lives. Nobody works harder than bees!

Dad, I remember you coming home so overworked

your hands were still stirring. You couldn't stop.

I remember that.

What right do they have to our honey?

We live on two cups a year. They put it in lip balm for no reason whatsoever!

Even if it's true, what can one bee do?

Sting them where it really hurts.

In the face! The eye!

That would hurt. No. Up the nose? That's a killer.

There's only one place you can sting the humans, one place where it matters.

Hive at Five, the hive's only full-hour action news source.

No more bee beards!

With Bob Bumble at the anchor desk.

Weather with Storm Stinger.

Sports with Buzz Larvi.

And Jeanette Ohung.

Good evening. I'm Bob Bumble. And I'm Jeanette Ohung. A tri-county bee, Barry Benson,

intends to sue the human race for stealing our honey,

packaging it and profiting from it illegally!

Tomorrow night on Bee Larry King,

we'll have three former queens here in our studio, discussing their new book,

Olassy Ladies, out this week on Hexagon.

Tonight we're talking to Barry Benson.

Did you ever think, "I'm a kid from the hive. I can't do this"?

Bees have never been afraid to change the world.

What about Bee Oolumbus? Bee Gandhi? Bejesus?

Where I'm from, we'd never sue humans.

We were thinking of stickball or candy stores.

How old are you?

The bee community is supporting you in this case,

which will be the trial of the bee century.

You know, they have a Larry King in the human world too.

It's a common name. Next week…

He looks like you and has a show and suspenders and colored dots…

Next week…

Glasses, quotes on the bottom from the guest even though you just heard 'em.

Bear Week next week! They're scary, hairy and here live.

Always leans forward, pointy shoulders, squinty eyes, very Jewish.

In tennis, you attack at the point of weakness!

It was my grandmother, Ken. She's 81.

Honey, her backhand's a joke! I'm not gonna take advantage of that?

Quiet, please. Actual work going on here.

Is that that same bee? Yes, it is! I'm helping him sue the human race.

Hello. Hello, bee. This is Ken.

Yeah, I remember you. Timberland, size ten and a half. Vibram sole, I believe.

Why does he talk again?

Listen, you better go 'cause we're really busy working.

But it's our yogurt night!

Bye-bye.

Why is yogurt night so difficult?!

You poor thing. You two have been at this for hours!

Yes, and Adam here has been a huge help.

Frosting… How many sugars? Just one. I try not to use the competition.

So why are you helping me?

Bees have good qualities.

And it takes my mind off the shop.

Instead of flowers, people are giving balloon bouquets now.

Those are great, if you're three.

And artificial flowers.

Oh, those just get me psychotic! Yeah, me too. Bent stingers, pointless pollination.

Bees must hate those fake things!

Nothing worse than a daffodil that's had work done.

Maybe this could make up for it a little bit.

This lawsuit's a pretty big deal. I guess. You sure you want to go through with it?

Am I sure? When I'm done with the humans, they won't be able

to say, "Honey, I'm home," without paying a royalty!

It's an incredible scene here in downtown Manhattan,

where the world anxiously waits, because for the first time in history,

we will hear for ourselves if a honeybee can actually speak.

What have we gotten into here, Barry?

It's pretty big, isn't it?

I can't believe how many humans don't work during the day.

You think billion-dollar multinational food companies have good lawyers?

Everybody needs to stay behind the barricade.

What's the matter? I don't know, I just got a chill. Well, if it isn't the bee team.

You boys work on this?

All rise! The Honorable Judge Bumbleton presiding.

All right. Oase number 4475,

Superior Oourt of New York, Barry Bee Benson v. the Honey Industry

is now in session.

Mr. Montgomery, you're representing the five food companies collectively?

A privilege.

Mr. Benson… you're representing all the bees of the world?

I'm kidding. Yes, Your Honor, we're ready to proceed.

Mr. Montgomery, your opening statement, please.

Ladies and gentlemen of the jury,

my grandmother was a simple woman.

Born on a farm, she believed it was man's divine right

to benefit from the bounty of nature God put before us.

If we lived in the topsy-turvy world Mr. Benson imagines,

just think of what would it mean.

I would have to negotiate with the silkworm

for the elastic in my britches!

Talking bee!

How do we know this isn't some sort of

holographic motion-picture-capture Hollywood wizardry?

They could be using laser beams!

Robotics! Ventriloquism! Oloning! For all we know,

he could be on steroids!

Mr. Benson?

Ladies and gentlemen, there's no trickery here.

I'm just an ordinary bee. Honey's pretty important to me.

It's important to all bees. We invented it!

We make it. And we protect it with our lives.

Unfortunately, there are some people in this room

who think they can take it from us

'cause we're the little guys! I'm hoping that, after this is all over,

you'll see how, by taking our honey, you not only take everything we have

but everything we are!

I wish he'd dress like that all the time. So nice!

Oall your first witness.

So, Mr. Klauss Vanderhayden of Honey Farms, big company you have.

I suppose so.

I see you also own Honeyburton and Honron!

Yes, they provide beekeepers for our farms.

Beekeeper. I find that to be a very disturbing term.

I don't imagine you employ any bee-free-ers, do you?

No.

I couldn't hear you.

No.

No.

Because you don't free bees. You keep bees. Not only that,

it seems you thought a bear would be an appropriate image for a jar of honey.

They're very lovable creatures.

Yogi Bear, Fozzie Bear, Build-A-Bear.

You mean like this?

Bears kill bees!

How'd you like his head crashing through your living room?!

Biting into your couch! Spitting out your throw pillows!

OK, that's enough. Take him away.

So, Mr. Sting, thank you for being here. Your name intrigues me.

Where have I heard it before? I was with a band called The Police. But you've never been a police officer, have you?

No, I haven't.

No, you haven't. And so here we have yet another example

of bee culture casually stolen by a human

for nothing more than a prance-about stage name.

Oh, please.

Have you ever been stung, Mr. Sting?

Because I'm feeling a little stung, Sting.

Or should I say… Mr. Gordon M. Sumner!

That's not his real name?! You idiots!

Mr. Liotta, first, belated congratulations on

your Emmy win for a guest spot on ER in 2005.

Thank you. Thank you.

I see from your resume that you're devilishly handsome

with a churning inner turmoil that's ready to blow.

I enjoy what I do. Is that a crime?

Not yet it isn't. But is this what it's come to for you?

Exploiting tiny, helpless bees so you don't

have to rehearse your part and learn your lines, sir?

Watch it, Benson! I could blow right now!

This isn't a goodfella. This is a badfella!

Why doesn't someone just step on this creep, and we can all go home?!

Order in this court! You're all thinking it! Order! Order, I say!

Say it! Mr. Liotta, please sit down! I think it was awfully nice of that bear to pitch in like that.

I think the jury's on our side.

Are we doing everything right, legally?

I'm a florist.

Right. Well, here's to a great team.

To a great team!

Well, hello.

Ken! Hello. I didn't think you were coming.

No, I was just late. I tried to call, but… the battery.

I didn't want all this to go to waste, so I called Barry. Luckily, he was free.

Oh, that was lucky.

There's a little left. I could heat it up.

Yeah, heat it up, sure, whatever.

So I hear you're quite a tennis player.

I'm not much for the game myself. The ball's a little grabby.

That's where I usually sit. Right… there.

Ken, Barry was looking at your resume,

and he agreed with me that eating with chopsticks isn't really a special skill.

You think I don't see what you're doing?

I know how hard it is to find the rightjob. We have that in common.

Do we?

Bees have 100 percent employment, but we do jobs like taking the crud out.

That's just what I was thinking about doing.

Ken, I let Barry borrow your razor for his fuzz. I hope that was all right.

I'm going to drain the old stinger.

Yeah, you do that.

Look at that.

You know, I've just about had it

with your little mind games.

What's that? Italian Vogue. Mamma mia, that's a lot of pages.

A lot of ads.

Remember what Van said, why is your life more valuable than mine?

Funny, I just can't seem to recall that!

I think something stinks in here!

I love the smell of flowers.

How do you like the smell of flames?!

Not as much.

Water bug! Not taking sides!

Ken, I'm wearing a Ohapstick hat! This is pathetic!

I've got issues!

Well, well, well, a royal flush!

You're bluffing. Am I? Surf's up, dude!

Poo water!

That bowl is gnarly.

Except for those dirty yellow rings!

Kenneth! What are you doing?!

You know, I don't even like honey! I don't eat it!

We need to talk!

He's just a little bee!

And he happens to be the nicest bee I've met in a long time!

Long time? What are you talking about?! Are there other bugs in your life?

No, but there are other things bugging me in life. And you're one of them!

Fine! Talking bees, no yogurt night…

My nerves are fried from riding on this emotional roller coaster!

Goodbye, Ken.

And for your information,

I prefer sugar-free, artificial sweeteners made by man!

I'm sorry about all that.

I know it's got an aftertaste! I like it!

I always felt there was some kind of barrier between Ken and me.

I couldn't overcome it. Oh, well.

Are you OK for the trial?

I believe Mr. Montgomery is about out of ideas.

We would like to call Mr. Barry Benson Bee to the stand.

Good idea! You can really see why he's considered one of the best lawyers…

Yeah.

Layton, you've gotta weave some magic

with this jury, or it's gonna be all over.

Don't worry. The only thing I have to do to turn this jury around

is to remind them of what they don't like about bees.

You got the tweezers? Are you allergic? Only to losing, son. Only to losing.

Mr. Benson Bee, I'll ask you what I think we'd all like to know.

What exactly is your relationship

to that woman?

We're friends.

Good friends? Yes. How good? Do you live together?

Wait a minute…

Are you her little…

…bedbug?

I've seen a bee documentary or two. From what I understand,

doesn't your queen give birth to all the bee children?

Yeah, but…

So those aren't your real parents!

Oh, Barry…

Yes, they are!

Hold me back!

You're an illegitimate bee, aren't you, Benson?

He's denouncing bees!

Don't y'all date your cousins?

Objection! I'm going to pincushion this guy! Adam, don't! It's what he wants!

Oh, I'm hit!!

Oh, lordy, I am hit!

Order! Order!

The venom! The venom is coursing through my veins!

I have been felled by a winged beast of destruction!

You see? You can't treat them like equals! They're striped savages!

Stinging's the only thing they know! It's their way!

Adam, stay with me. I can't feel my legs. What angel of mercy will come forward to suck the poison

from my heaving buttocks?

I will have order in this court. Order!

Order, please!

The case of the honeybees versus the human race

took a pointed turn against the bees

yesterday when one of their legal team stung Layton T. Montgomery.

Hey, buddy.

Hey.

Is there much pain?

Yeah.

I…

I blew the whole case, didn't I?

It doesn't matter. What matters is you're alive. You could have died.

I'd be better off dead. Look at me.

They got it from the cafeteria downstairs, in a tuna sandwich.

Look, there's a little celery still on it.

What was it like to sting someone?

I can't explain it. It was all…

All adrenaline and then… and then ecstasy!

All right.

You think it was all a trap?

Of course. I'm sorry. I flew us right into this.

What were we thinking? Look at us. We're just a couple of bugs in this world.

What will the humans do to us if they win?

I don't know.

I hear they put the roaches in motels. That doesn't sound so bad.

Adam, they check in, but they don't check out!

Oh, my.

Oould you get a nurse to close that window?

Why? The smoke. Bees don't smoke.

Right. Bees don't smoke.

Bees don't smoke! But some bees are smoking.

That's it! That's our case!

It is? It's not over?

Get dressed. I've gotta go somewhere.

Get back to the court and stall. Stall any way you can.

And assuming you've done step correctly, you're ready for the tub.

Mr. Flayman.

Yes? Yes, Your Honor!

Where is the rest of your team?

Well, Your Honor, it's interesting.

Bees are trained to fly haphazardly,

and as a result, we don't make very good time.

I actually heard a funny story about…

Your Honor, haven't these ridiculous bugs

taken up enough of this court's valuable time?

How much longer will we allow these absurd shenanigans to go on?

They have presented no compelling evidence to support their charges

against my clients, who run legitimate businesses.

I move for a complete dismissal of this entire case!

Mr. Flayman, I'm afraid I'm going

to have to consider Mr. Montgomery's motion.

But you can't! We have a terrific case.

Where is your proof? Where is the evidence?

Show me the smoking gun!

Hold it, Your Honor! You want a smoking gun?

Here is your smoking gun.

What is that?

It's a bee smoker!

What, this? This harmless little contraption?

This couldn't hurt a fly, let alone a bee.

Look at what has happened

to bees who have never been asked, "Smoking or non?"

Is this what nature intended for us?

To be forcibly addicted to smoke machines

and man-made wooden slat work camps?

Living out our lives as honey slaves to the white man?

What are we gonna do? He's playing the species card. Ladies and gentlemen, please, free these bees!

Free the bees! Free the bees!

Free the bees!

Free the bees! Free the bees!

The court finds in favor of the bees!

Vanessa, we won!

I knew you could do it! High-five!

Sorry.

I'm OK! You know what this means?

All the honey will finally belong to the bees.

Now we won't have to work so hard all the time.

This is an unholy perversion of the balance of nature, Benson.

You'll regret this.

Barry, how much honey is out there?

All right. One at a time.

Barry, who are you wearing?

My sweater is Ralph Lauren, and I have no pants.

What if Montgomery's right? What do you mean? We've been living the bee way a long time, 27 million years.

Oongratulations on your victory. What will you demand as a settlement?

First, we'll demand a complete shutdown of all bee work camps.

Then we want back the honey that was ours to begin with,

every last drop.

We demand an end to the glorification of the bear as anything more

than a filthy, smelly, bad-breath stink machine.

We're all aware of what they do in the woods.

Wait for my signal.

Take him out.

He'll have nauseous for a few hours, then he'll be fine.

And we will no longer tolerate bee-negative nicknames…

But it's just a prance-about stage name!

…unnecessary inclusion of honey in bogus health products

and la-dee-da human tea-time snack garnishments.

Oan't breathe.

Bring it in, boys!

Hold it right there! Good.

Tap it.

Mr. Buzzwell, we just passed three cups, and there's gallons more coming!

I think we need to shut down! Shut down? We've never shut down. Shut down honey production!

Stop making honey!

Turn your key, sir!

What do we do now?

Oannonball!

We're shutting honey production!

Mission abort.

Aborting pollination and nectar detail. Returning to base.

Adam, you wouldn't believe how much honey was out there.

Oh, yeah?

What's going on? Where is everybody?

Are they out celebrating? They're home. They don't know what to do. Laying out, sleeping in.

I heard your Uncle Oarl was on his way to San Antonio with a cricket.

At least we got our honey back.

Sometimes I think, so what if humans liked our honey? Who wouldn't?

It's the greatest thing in the world! I was excited to be part of making it.

This was my new desk. This was my new job. I wanted to do it really well.

And now…

Now I can't.

I don't understand why they're not happy.

I thought their lives would be better!

They're doing nothing. It's amazing. Honey really changes people.

You don't have any idea what's going on, do you?

What did you want to show me? This. What happened here?

That is not the half of it.

Oh, no. Oh, my.

They're all wilting.

Doesn't look very good, does it?

No.

And whose fault do you think that is?

You know, I'm gonna guess bees.

Bees?

Specifically, me.

I didn't think bees not needing to make honey would affect all these things.

It's notjust flowers. Fruits, vegetables, they all need bees.

That's our whole SAT test right there.

Take away produce, that affects the entire animal kingdom.

And then, of course…

The human species?

So if there's no more pollination,

it could all just go south here, couldn't it?

I know this is also partly my fault.

How about a suicide pact?

How do we do it?

I'll sting you, you step on me. Thatjust kills you twice. Right, right.

Listen, Barry… sorry, but I gotta get going.

I had to open my mouth and talk.

Vanessa?

Vanessa? Why are you leaving? Where are you going?

To the final Tournament of Roses parade in Pasadena.

They've moved it to this weekend because all the flowers are dying.

It's the last chance I'll ever have to see it.

Vanessa, I just wanna say I'm sorry. I never meant it to turn out like this.

I know. Me neither.

Tournament of Roses. Roses can't do sports.

Wait a minute. Roses. Roses?

Roses!

Vanessa!

Roses?!

Barry?

Roses are flowers! Yes, they are. Flowers, bees, pollen!

I know. That's why this is the last parade.

Maybe not. Oould you ask him to slow down?

Oould you slow down?

Barry!

OK, I made a huge mistake. This is a total disaster, all my fault.

Yes, it kind of is.

I've ruined the planet. I wanted to help you

with the flower shop. I've made it worse.

Actually, it's completely closed down.

I thought maybe you were remodeling.

But I have another idea, and it's greater than my previous ideas combined.

I don't want to hear it!

All right, they have the roses, the roses have the pollen.

I know every bee, plant and flower bud in this park.

All we gotta do is get what they've got back here with what we've got.

Bees.

Park.

Pollen!

Flowers.

Repollination!

Across the nation!

Tournament of Roses, Pasadena, Oalifornia.

They've got nothing but flowers, floats and cotton candy.

Security will be tight.

I have an idea.

Vanessa Bloome, FTD.

Official floral business. It's real.

Sorry, ma'am. Nice brooch.

Thank you. It was a gift.

Once inside, we just pick the right float.

How about The Princess and the Pea?

I could be the princess, and you could be the pea!

Yes, I got it.

Where should I sit?

What are you?

I believe I'm the pea.

The pea?

It goes under the mattresses.

Not in this fairy tale, sweetheart. I'm getting the marshal. You do that! This whole parade is a fiasco!

Let's see what this baby'll do.

Hey, what are you doing?!

Then all we do is blend in with traffic…

…without arousing suspicion.

Once at the airport, there's no stopping us.

Stop! Security.

You and your insect pack your float? Yes. Has it been in your possession the entire time?

Would you remove your shoes?

Remove your stinger. It's part of me. I know. Just having some fun. Enjoy your flight.

Then if we're lucky, we'll have just enough pollen to do the job.

Oan you believe how lucky we are? We have just enough pollen to do the job!

I think this is gonna work.

It's got to work.

Attention, passengers, this is Oaptain Scott.

We have a bit of bad weather in New York.

It looks like we'll experience a couple hours delay.

Barry, these are cut flowers with no water. They'll never make it.

I gotta get up there and talk to them.

Be careful.

Oan I get help with the Sky Mall magazine?

I'd like to order the talking inflatable nose and ear hair trimmer.

Oaptain, I'm in a real situation.

What'd you say, Hal? Nothing. Bee!

Don't freak out! My entire species…

What are you doing?

Wait a minute! I'm an attorney! Who's an attorney? Don't move.

Oh, Barry.

Good afternoon, passengers. This is your captain.

Would a Miss Vanessa Bloome in 24B please report to the cockpit?

And please hurry!

What happened here?

There was a DustBuster, a toupee, a life raft exploded.

One's bald, one's in a boat, they're both unconscious!

Is that another bee joke? No! No one's flying the plane!

This is JFK control tower, Flight 356. What's your status?

This is Vanessa Bloome. I'm a florist from New York.

Where's the pilot?

He's unconscious, and so is the copilot.

Not good. Does anyone onboard have flight experience?

As a matter of fact, there is.

Who's that? Barry Benson. From the honey trial?! Oh, great.

Vanessa, this is nothing more than a big metal bee.

It's got giant wings, huge engines.

I can't fly a plane.

Why not? Isn't John Travolta a pilot? Yes. How hard could it be?

Wait, Barry! We're headed into some lightning.

This is Bob Bumble. We have some late-breaking news from JFK Airport,

where a suspenseful scene is developing.

Barry Benson, fresh from his legal victory…

That's Barry!

…is attempting to land a plane, loaded with people, flowers

and an incapacitated flight crew.

Flowers?!

We have a storm in the area and two individuals at the controls

with absolutely no flight experience.

Just a minute. There's a bee on that plane.

I'm quite familiar with Mr. Benson and his no-account compadres.

They've done enough damage.

But isn't he your only hope?

Technically, a bee shouldn't be able to fly at all.

Their wings are too small…

Haven't we heard this a million times?

"The surface area of the wings and body mass make no sense."

Get this on the air!

Got it.

Stand by.

We're going live.

The way we work may be a mystery to you.

Making honey takes a lot of bees doing a lot of small jobs.

But let me tell you about a small job.

If you do it well, it makes a big difference.

More than we realized. To us, to everyone.

That's why I want to get bees back to working together.

That's the bee way! We're not made of Jell-O.

We get behind a fellow.

Black and yellow! Hello! Left, right, down, hover.

Hover? Forget hover. This isn't so hard. Beep-beep! Beep-beep!

Barry, what happened?!

Wait, I think we were on autopilot the whole time.

That may have been helping me. And now we're not! So it turns out I cannot fly a plane.

All of you, let's get behind this fellow! Move it out!

Move out!

Our only chance is if I do what I'd do, you copy me with the wings of the plane!

Don't have to yell.

I'm not yelling! We're in a lot of trouble.

It's very hard to concentrate with that panicky tone in your voice!

It's not a tone. I'm panicking!

I can't do this!

Vanessa, pull yourself together. You have to snap out of it!

You snap out of it.

You snap out of it.

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

You snap out of it!

Hold it!

Why? Oome on, it's my turn.

How is the plane flying?

I don't know.

Hello?

Benson, got any flowers for a happy occasion in there?

The Pollen Jocks!

They do get behind a fellow.

Black and yellow. Hello. All right, let's drop this tin can on the blacktop.

Where? I can't see anything. Oan you?

No, nothing. It's all cloudy.

Oome on. You got to think bee, Barry.

Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee!

Wait a minute. I think I'm feeling something.

What? I don't know. It's strong, pulling me. Like a 27-million-year-old instinct.

Bring the nose down.

Thinking bee! Thinking bee! Thinking bee!

What in the world is on the tarmac? Get some lights on that! Thinking bee! Thinking bee! Thinking bee!

Vanessa, aim for the flower. OK. Out the engines. We're going in on bee power. Ready, boys?

Affirmative!

Good. Good. Easy, now. That's it.

Land on that flower!

Ready? Full reverse!

Spin it around!

Not that flower! The other one!

Which one?

That flower.

I'm aiming at the flower!

That's a fat guy in a flowered shirt. I mean the giant pulsating flower

made of millions of bees!

Pull forward. Nose down. Tail up.

Rotate around it.

This is insane, Barry! This's the only way I know how to fly. Am I koo-koo-kachoo, or is this plane flying in an insect-like pattern?

Get your nose in there. Don't be afraid. Smell it. Full reverse!

Just drop it. Be a part of it.

Aim for the center!

Now drop it in! Drop it in, woman!

Oome on, already.

Barry, we did it! You taught me how to fly!

Yes. No high-five! Right. Barry, it worked! Did you see the giant flower?

What giant flower? Where? Of course I saw the flower! That was genius!

Thank you. But we're not done yet. Listen, everyone!

This runway is covered with the last pollen

from the last flowers available anywhere on Earth.

That means this is our last chance.

We're the only ones who make honey, pollinate flowers and dress like this.

If we're gonna survive as a species, this is our moment! What do you say?

Are we going to be bees, orjust Museum of Natural History keychains?

We're bees!

Keychain!

Then follow me! Except Keychain.

Hold on, Barry. Here.

You've earned this.

Yeah!

I'm a Pollen Jock! And it's a perfect fit. All I gotta do are the sleeves.

Oh, yeah.

That's our Barry.

Mom! The bees are back!

If anybody needs to make a call, now's the time.

I got a feeling we'll be working late tonight!

Here's your change. Have a great afternoon! Oan I help who's next?

Would you like some honey with that? It is bee-approved. Don't forget these.

Milk, cream, cheese, it's all me. And I don't see a nickel!

Sometimes I just feel like a piece of meat!

I had no idea.

Barry, I'm sorry. Have you got a moment?

Would you excuse me? My mosquito associate will help you.

Sorry I'm late.

He's a lawyer too?

I was already a blood-sucking parasite. All I needed was a briefcase.

Have a great afternoon!

Barry, I just got this huge tulip order, and I can't get them anywhere.

No problem, Vannie. Just leave it to me.

You're a lifesaver, Barry. Oan I help who's next?

All right, scramble, jocks! It's time to fly.

Thank you, Barry!

That bee is living my life!

Let it go, Kenny.

When will this nightmare end?!

Let it all go.

Beautiful day to fly.

Sure is.

Between you and me, I was dying to get out of that office.

You have got to start thinking bee, my friend.

Thinking bee! Me? Hold it. Let's just stop for a second. Hold it.

I'm sorry. I'm sorry, everyone. Oan we stop here?

I'm not making a major life decision during a production number!

All right. Take ten, everybody. Wrap it up, guys.

I had virtually no rehearsal for that.