Visuomotor decision-making through multifeature convergence in the larval zebrafish hindbrain

Domain: chemistry
Published: 2026-03-05
Editorial outcome: major_revision

Abstract
Animals continuously extract and evaluate diverse sensory information from the environment to guide behavior. Yet, how neural circuits integrate multiple, potentially conflicting, inputs remains poorly understood. Here, we use larval zebrafish to address this question, leveraging their robust optomotor response to coherent random dot motion and phototaxis towards light. We demonstrate that animals employ an additive behavioral algorithm of three visual features: motion coherence, luminance level, and changes in luminance. Using brain-wide two-photon imaging, we identify the loci of these computations, with the anterior hindbrain emerging as a multifeature integration hub. Through single-cell neurotransmitter and morphological analyses of functionally identified neurons, we characterize potential connections within and across computational nodes. These experiments reveal three parallel and converging computational pathways, matching our behavioral results. Our study provides a mechanistic brain-wide account of how a vertebrate brain integrates multiple features to drive sensorimotor decisions, bridging behavioral algorithms with their neural implementation.

Introduction
Animals continuously process noisy and often conflicting sensory information from their complex, dynamic environment to guide movement decisions. The precise neural mechanisms that allow the brain to extract and computationally process such cues remain poorly understood.

Multiple sensory streams can be combined through, for example, additive or winner-takes-all algorithms. For instance, when an animal attends to different targets, directing its gaze toward the averaged location can provide an effective solution1,2. Such an additive strategy has been observed across various species3–7. In experiments where animals need to combine multiple sensory features during a motor control decision-making task, additive mechanisms reliably encode inputs across the different sensory features? To investigate this question, we systematically paired coherent random dot stimuli of varying strengths with either a congruent or a conflicting spatial luminance cue.

To address this topic, larval zebrafish, with their accessible nervous system, form an effective model to dissect the behavioral algorithms and neural mechanisms that underlie multifeature sensory processing within the visual modality. Previous studies in larval zebrafish have explored the integration of separate sensory stimuli, identifying the anterior hindbrain and adjacent regions as a key site of higher-order sensory processing and behavioral control: Temporal integration of visual motion cues16,17, processing of heat18, of utility19, of luminance20,21, of internal states related to the optokinetic reflex22, as well as of opposing threatening looming cues11. In addition, this brain area controls exploratory locomotion states23 and represents integrated heading direction information for behavioral navigation24. Using a variety of visual stimuli within a single experiment, it has also been shown that stimulus representations overlap within the anterior hindbrain region20. However, it remains unclear how this brain area contributes to the extraction of visual features when multiple stimulus types are presented simultaneously and how it resolves situations in which sensory cues are presented in a conflicting configuration.

In this work, we disentangle how motion and luminance are integrated simultaneously in the larval zebrafish brain, combining two ethologically relevant behaviors: the optomotor response and phototaxis. The optomotor response is a position-stabilizing behavior in response to whole-field visual motion in which fish prefer to follow the direction of motion25. Whereas phototaxis allows larvae to navigate to optimal light conditions, usually preferring brighter parts26,27. Both stimuli can drive directional swimming26,28–31, and previous work has provided a good understanding of the brain regions and computations involved16,17,20,25,32–35. Our goal is to determine how these circuits interact. Are visual motion and luminance cues processed in parallel, and where do representations converge? Or are signals combined early on according to their directional influence on behavioral choice? Does a winner-takes-all competition resolve sensory conflict, or are inputs simply added to guide motor decisions? Are conflicting stimulus situations processed differently from congruent ones?

In this work, we address these questions by first using precisely controlled behavioral experiments to construct a multifeature integration model. We then use our model to generate specific circuit hypotheses about the underlying neural computations in the brain. Based on such information, through brain-wide functional microscopy, we then search for respective representations. Starting at the identified loci, we then characterize neuronal anatomies, neurotransmitter distributions, and potential connectivity arrangements across the computational units, allowing us to update and further refine our model. Thus, our study provides a comprehensive account that links behavioral experiments to neural circuit structure and function, enabling us to uncover a biologically plausible algorithmic and mechanistic model of multifeature sensorimotor decision-making in larval zebrafish.

Results
Addition of multifeature sensory cues captures sensorimotor decision-making

Larval zebrafish swim in discrete bouts and adjust their heading direction spontaneously and based on external sensory cues. While visual motion drift and spatial luminance cues are both known to directionally guide animals, it remains unclear how zebrafish integrate these two stimuli when presented at the same time. Do they prioritize the strongest cue through a winner-takes-all strategy, or do they sum the linear sum of responses to either cue alone. Based on these results, we hypothesize that larval zebrafish employ at least three parallel computational pathways that process visual cues during sensorimotor decision-making: (1) temporally integrated motion (named motion pathway), (2) lateral luminance levels (named luminance level pathway), as well as (3) lateral luminance change (named luminance change pathway). Since the fish mostly swim in the direction of coherent motion, and display a steady-state tendency to turn towards the brighter side, we consider visual motion cues and steady-state bright lateral luminance cues to be attractive. During the transient phase of luminance change, fish swim away from changes in luminance level. Therefore, sudden changes in lateral luminance seem to be repulsive to larvae. Finally, we propose that the output of these computational pathways converges without explicit mutual interactions to drive directional swimming behavior.

To formalize this hypothesis, we developed an algorithmic model (Fig. 2e) incorporating a weighted sum of these three processing pathways. We implemented the luminance change pathway as a sign-invariant derivative, modeled as two rectifying units that compare the current luminance level with its temporally integrated form (Fig. 2e, scheme on the right). The model includes seven free parameters: a weight and a time constant per processing pathway, and one additional time constant for a multifeature integration hub where pathways converge.

We fitted our model to experimental data and validated results through a leave-out-and-testing approach. We designed a set of 40 stimuli in which motion and luminance varied across several spatial and temporal configurations (Fig. 2f, Supplementary Fig. 2a, and Methods). Stimuli classes are only-motion (M), only-luminance (L), congruent (M=L), or conflicting (M≠L). We fitted our model to the behavioral dynamics in response to a subset of 20 stimuli across all four stimulus classes (multifeature training, Fig. 2f). We then used the remaining 20 stimuli (multifeature testing) to validate the quality of the fit by computing the mean-squared error between model prediction and experimental data. Our analysis revealed that the experimental data closely matched the model prediction (Fig. 2g,h). 

We next probed whether nonlinear interactions across the three different processing pathways in our model may influence behavior. If no nonlinear interaction exists, then a model trained separately on only-motion and only-luminance trials should be able to faithfully predict responses to combined stimuli. In contrast, if major crosstalk occurs, then this unifeature training should result in significantly worse predictions for the combined stimuli. We trained our model on 20 stimuli selected from the only-motion (M) and only-luminance (L) classes (unifeature-training, Fig. 2f). Notably, this unifeature-trained model performed just as well as the multifeature-trained model (Fig. 2g,h and Supplementary Fig. 2b), further corroborating the independent processing of visual information across the three computational pathways.

To check for the relative importance of each pathway, we performed in silico silencing simulations. To this end, we systematically switched off individual computational pathways in our model (Fig. 2h). Removing either the lateral luminance change or the lateral luminance level pathway had small but significant effects, reducing the quality of the fit. Removing the motion integration pathway strongly reduced model performance. We thus conclude that all three parts of our model are required to explain the observed behavioral dynamics.

With our fitting approach, we obtained reliable estimates of the seven parameters (Fig. 2i,j): The time constants for the three parallel processing pathways and the multifeature integration hub had a similar order of magnitude, with the luminance change pathway time constant being slightly higher (Fig. 2i). In agreement with our model unit silencing tests (Fig. 2h), motion carried the strongest weight (Fig. 2j). The weight for the lateral luminance level pathway played a positive, but relatively minor role. The weight for the lateral luminance change pathway was close to the weight for the motion pathway. The high weight of this pathway but the observed modest effect when silenced can be explained because the lateral luminance change pathway is only active during the short time window at stimulus transitions (Supplementary Fig. 2c).

In summary, our analyses of the behavioral dynamics across the entire period of stimulus presentation support an algorithm involving the parallel integration of three visual streams: temporally integrated directional motion cues, lateral luminance levels, and lateral luminance change. While motion and bright lateral luminance cues are attractive, larvae are repelled from the side at which luminance changes occur. A simple generative model that adds and convergently integrates these processes could successfully predict behavioral response curves for a large variety of superimposed motion and lateral luminance stimuli. Having established this computational framework, we next investigated brain-wide neural activity to identify circuits that correspond to the nodes of our model.

Model-predicted activity is represented across the brain

Since our model accurately predicted behavioral dynamics to a wide range of stimulus configurations, we next sought to determine whether and where its computational components may be represented in the larval zebrafish brain. To achieve this, we performed two-photon calcium imaging in elavl3:H2B-GCaMP8s larvae (Fig. 3a and

Methods
We presented visual stimuli to the bottom of 12cm diameter arenas where unconstrained individual larvae could freely swim. A high-speed camera tracked animals in real time (Fig. 1a and Supplementary Movie 1). We detected swim bouts based on increases in the variance of body orientation (Fig. 1b), with each bout considered a decision event. To simplify our analysis, we binarized bouts based on orientation change; left (<–2°) or right (>2°) (Fig. 1c, top). Swim bouts occurred in stochastic interbout intervals at around 2Hz (Fig. 1c, bottom). To allow for variations of strength in the motion stimulus, we used random dot kinematograms of varying coherence levels. Certain fractions of dots moved coherently right- or leftward. All dots had short lifetimes and got randomly repositioned in the arena, preventing animals from tracking individual dots. We superimposed coherent motion stimuli with lateral luminance cues and locked stimuli to the body orientation of animals in real-time (Fig. 1d). This design ensures that during the stimulus, input into the visual system remains stable over time from the perspective of the fish, providing a consistent and well-controlled configuration for the study of sensorimotor decision-making. We initially focused our analysis of directional swimming during the steady-state phase within the last 10s of stimulus presentation.

To uncover the computational principles underlying multifeature stimulus integration, we analyzed zebrafish behavior as a function of coherent motion strength with or without superimposed lateralized luminance cues. With higher coherence levels, animals should be more likely to follow motion cues16,17. The behavioral decision curve in response to motion alone (= only-motion) resembles a psychometric curve (Fig. 1e, f) in which stronger rightward motion results in a higher ratio of rightward swims and vice versa for leftward motion. What would happen to this curve if we superimpose lateral luminance cues? Luminance is generally attractive26,27. Therefore, if zebrafish use an additive strategy, then we would expect that lateral luminance cues in which the right side is brighter than the left side would shift this curve upward. For lateral luminance cues of flipped polarity, we expect to see the curve shift downward (Fig. 1e). If instead fish follow a winner-takes-all strategy, responses should match the only-motion curve for strong coherence levels but switch to following lateral luminance cues at low coherences (Fig. 1f). We fitted a sigmoidal curve with additive or winner-takes-all inputs8 (Methods) to make quantitative predictions for both these scenarios. Our behavioral experiments and model-fitting results (Fig. 1g) showed that, on average, zebrafish follow the additive algorithm.

It may be possible that the observed effects originate from combining animals that employ distinct strategies, where some fish favor motion while others rely more on luminance. To test this idea, we fitted our multifeature model to individual fish. We fitted the additive and winner-takes-all version of our model, as well as the motion-only and luminance-only versions, to account for the possibility of some individual fish relying on motion and others on luminance. While individuals exhibited variability in their response curves, the additive model described behavior better than the winner-takes-all model and single stimulus model-variations (MOT: only-motion, LUMI: only-luminance) in 76 out of the 79 tested larvae (Fig. 1h, i).

To further explore which sensory tuning parameters, such as decision-threshold and sensitivity, changes upon combining the two stimuli, we performed an additional analysis using the threshold and width parametrization of the psychometric curve36,37 (Methods). This analysis revealed that superimposing lateral luminance cues to the motion stimulus only shifts the decision threshold while leaving coding sensitivity intact (Supplementary Fig. 1a–c), further corroborating that both stimulus features are independently processed.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study from the Bahl lab, the authors investigated how zebrafish larvae integrate multiple visual features (motion coherence, luminance level, and changes in luminance) while making decisions that guide swimming behavior. They first presented a clever set of experiments to carefully dissect visual feature integration in freely swimming zebrafish larvae. Based on the results of these behavioral experiments, they developed a model that mimics the animals’ decision-making process. Subsequently, they used two-photon microscopy to identify neurons across the zebrafish brain that correspond to different nodes of the model. They discovered several such neurons with diverse properties in the midbrain and hindbrain circuitry. The combined results revealed that zebrafish employ an additive strategy, rather than a winner-takes-all approach, when processing these visual cues. The authors identified three parallel sensory pathways (motion, luminance level, and luminance change) that converge in the anterior hindbrain, which acts as a multi-feature integration hub. Interestingly, an earlier study showed that neurons in this anterior hindbrain region are related to the head direction system, further highlighting the importance of this area. One of the most exciting aspects of this study is the experimental design that enabled photo-labeling of functionally identified groups of neurons. This allowed the authors to determine the morphologies of functionally defined neurons. By combining open neural morphology data with their own functionally labeled anatomical data, the authors refined their working model, which is presented at the end of the paper and illustrates multi-feature integration of visual stimuli.\nI believe this is an exciting study, paving the way for new approaches to studying brain networks. I am particularly enthusiastic about the authors’ photo-activation approach, which enabled them to identify the morphologies of functionally defined neurons, a non-trivial task. The study has great potential, and I have a few comments and suggestions that I hope will help improve it:\nComments:\n1. The biggest strength of the manuscript, the photo-labeling of functionally defined neurons, is not presented in sufficient detail to assess the quality and clarity of the method. For example, it is unclear how effective this labeling is in targeting the intended neurons. What is the success rate? How confident can we be that the labeled neuron is indeed the targeted one? As it stands, the presentation lacks sufficient clarity to evaluate potential pitfalls and the overall quality of this method.\n2. Similarly, the quality of HCR matching to individual functionally imaged neurons is difficult to assess. Would it not be better to complement these HCR results with dual-channel imaging of calcium signals and neuronal identity using relevant vGlut and Gad1 transgenic lines?\n3. The statement “Based on our previous structure-to-function analyses of motion processing neurons in the anterior hindbrain, we model these connections as likely inhibitory” seems to rely on a limited number of data points, expanding this data could be important. Is there no variability in the anatomy of functionally identified neurons? Are they truly so similar?\n4. The anatomical descriptions are very zebrafish-centric and difficult to relate to general concepts of the vertebrate brain. Could the authors expand the discussion to relate their findings to what is known about vertebrate hindbrain circuits?\n5. The authors state: “Furthermore, we conclude that motion and luminance cues jointly control swim initiation, leading to delayed responses in conflicting conditions.” I could not find the specific data or results that support this conclusion. Could you please clarify?\n6. The paragraph starting at line 211 is difficult to read and lacks sufficient figure citations.\n7. The authors’ results highlight the anterior hindbrain, a brain region recently shown to contain the head direction system in zebrafish. It would be important for the authors to discuss how their findings align with this earlier work.\n8. The authors discuss visual responses in the habenula and cite a paper from the Julin Du lab, but they seem to have missed the earlier paper by Dreosti et al. (2014), which first showed such visual responses. Please consider including this reference.\n9. The y-axis in Figure 1I is not well defined.\n10. Figure legends need clearer explanations, and some color codes are difficult to interpret.\n11. The statements “We further find that the computation of lateral luminance change detection happens locally in the optic tectum” and “First, an excitatory connection of motion integrators onto multifeatured neurons without clear compartmentalization. Second, an inhibitory ipsilateral connection from lateral luminance level integrator cells onto multifeature neurons” are difficult to interpret. What specific datasets support these claims?\n12. Similarly, the claims about dendritic computations on line 524 are not substantiated with data. I recommend toning down these statements.", "reviewer_2": "Visuomotor decision-making through multifeature convergence in the larval zebrafish hindbrain\nThis study investigates the neural basis of multifeature visual integration in larval zebrafish. The authors combine precise behavioural assays with computational modeling to provide convincing evidence that zebrafish employ an additive algorithm to integrate three visual cues during sensorimotor decisions - coherent motion, steady-state luminance levels, and transient luminance changes. Using brain-wide two-photon calcium imaging, they map these computations to distinct neural populations, demonstrating motion and multifeature integration in the anterior hindbrain and luminance processing in the optic tectum. Complementary analyses of neurotransmitter identity (via HCR-FISH) and single-neuron morphology (via pa-GFP photoactivation) support a circuit model featuring parallel pathways that converge additively. The paper provides a biologically plausible framework linking behaviour to neural implementation, with broad implications for sensory integration across vertebrates. The work extends single-feature studies (e.g., motion in Bahl & Engert, Nat. Neurosci. 2020 [ref. 17]; luminance in Wolf et al., Nat. Commun. 2017 [ref. 22]) to multifeature contexts. Overall, I think that this is a very interesting paper that will have impact for future work across animal models used to investigate the neural basis of behaviour.\nSignificance: This advances our understanding of how vertebrate brains resolve sensory conflicts via parallel processing, with the anterior hindbrain emerging as a versatile integration hub. It addresses a core question in decision neuroscience—additive vs. competitive algorithms—while providing a blueprint for linking behaviour to circuits in compact brains. The multi-level analysis of this problem highlights the advantages of the larval zebrafish as a model. I can’t think of a study using mice that similalrly integrates behaviour, algorithmic modelling, brain-wide recording of activity (!) and pathway mapping. An excellent piece of work.\nMinor concerns and suggestions\nThe manuscript has been prepared to a very high standard and documents the work in detail. The strongest parts of the manuscript are the behavioural analysis, the algorithmic modelling, and the survey and analysis of neural responses. The latter sections that attempt to link neural morphology/transmitter type to the circuit model were less satisfying overall, in large part because they lacked any experimental tests (e.g by manipulating activity). But I don’t think it would be reasonable to ask for this to be added to this manuscript (although it might warrant a mention in the Discussion).\nThe manuscript is already very strong, but a few clarifications would polish it further:\n1. Figure 2 and model details: The luminance change pathway's high weight but modest silencing effect is well-explained, but quantifying its transient contribution (e.g., via integrated activity over stimulus edges) could strengthen the intuition. Consider adding a supplemental simulation showing pathway contributions across time.\n2. Neural classification: Logical statements are effective, but the thresholds (e.g., ΔdF/F0 > 0.1) feel somewhat arbitrary—briefly justify them in Methods (e.g., based on noise floors) or via a ROC curve in supplements. The winner-takes-all cells (Extended Data Fig. S3D) are intriguing; a sentence speculating on their recruitment (e.g., under time pressure) could tie to the discussion's flexibility theme.\n3. General: There is a tendency to repetition in the manuscript, especially in the Discussion. Readability could be improved by cutting some of this and making the Discussion more focused. For instance, it feels like there is some overreach in cross-species comparisons. The claim of “conserved principles” ignores algorithmic differences—mammalian multisensory integration often involves divisive normalization (Ohshiro et al., Nat. Neurosci. 2011 [ref. 57]), not just addition. The authors’ mention of sensory processing disorders feels cursory to this reviewer, although this is a matter of taste.\nLeon Lagnado", "reviewer_3": "The paper “Visuomotor decision-making through multifeatured convergence in the larval zebrafish hindbrain” by Slangewal et al., describes behavioral and functional responses across the zebrafish brain to reorientation-inducing visual stimuli. Given previous literature on motion integration and phototaxis, the data obtained in this manuscript aren’t necessarily surprising. However, the manuscript combines responses to luminance differences and lateral motion in a clever way. The central claim of the paper is that luminance differences and motion direction are processed in parallel pathways. Given the proposed architecture of visual processing parallel processing of features is not surprising. However, I am not sure if the presented data supports the term “parallel pathways” well enough, see below. While experiments and analysis are well done overall, I have strong concerns with some of the interpretations which need to be addressed before publication.\n-Major concerns-\nA major finding according to the authors is the integration of information from two parallel “pathways” a motion pathway and a luminance pathway. Given visual system architecture and responses, parallel processing is not surprising. However, the term “parallel pathways” suggests that information is kept separate across the circuits until it hits the “multifeature integrators.” The experiments conducted by the authors are not sufficient to draw this conclusion. The experiments demonstrate that at the level of behavior, there is no multiplicative interaction while the imaging experiments suggest a separation of the pathways, except the “multifeature integrators.” However, the experiments cannot show that these multifeature integrators are in fact at the end of pathways rather than in the middle, etc. In the end, a well-supported conclusion is that the information adds linearly and that there are neural populations that separately process the information as well as neural populations that might integrate the information (more on that below). I would therefore suggest the authors speak of “parallel processing” and “linear addition” instead of “parallel pathways.”\nPrevious work has demonstrated beautifully that neurons in the anterior hindbrain integrate lateral motion cues. A hallmark of this integration is that motion coherence affects the speed of integration but not the reachable level. In fact, this is critical information to conclude that a neural population integrates information. Simply showing that activity rises slowly is insufficient, especially with calcium imaging. It is therefore misleading to name the yellow population (top right) in Figure 3F “Luminance integrators” despite their match to model predictions for one luminance level. The authors will need to show that the activity slope within these neurons depends on luminance contrast while the level reached does not. Absent that, the neurons should be given a name that reflects that the neurons show sustained responses to luminance differences. This is the only conclusion that can be drawn given the current data.\nIn a similar vein, I don’t think that the blue population in the same figure (bottom right) can be confidently labeled “Multifeature integrators.” There are clear differences between the responses and the predictions which highlights an overall problem: How is it decided that the match “is good enough” to warrant the name given by the model?\nGiven the current analysis, this statement is misleading. To properly characterize the presumed “manifold”, the dimensionality of the data needs to be considered. Given the fraction of explained variance, it has to be assumed that this dimensionality is larger than 30 as 30 components only explain a little more than 50% of the variance. Statements about orthogonality will need to consider the full dimensionality. What the authors find in Figure S4 is therefore not a feature of the data but a feature of the analysis. This section does not add information while being used to draw a misleading conclusion - the section title suggests the existence of a “low-dimensional space” and “orthogonal processing.” I also do not believe that this information is important for the overall work - the authors already show a separation of encoding on the individual neuron level. Considering the model structure, this seems to be the more important result.\nSince a lot of the features discovered by the authors are predicted by prior work in the visual system, there should be a broader discussion on properties of visual responses and how these relate to the findings. The discussion instead seems to unnecessarily focus on highlighting perceived advances of the authors’ approach which may or may not be warranted.\n-Minor points-\n“Winner take-all responses”: The definition of the authors is not sufficient to label these types of neurons as “winner take-all.” The neurons the authors describe with this name are just linear adders (with negative sign), while “winner take-all” cells should respond to the more important feature, either in a constant or stochastic manner.\nNeuron identification by logical statements: This is a clever idea. However, the fact that fewer neurons are identified compared with regression is not enough to conclude that the approach is better or worse. Maybe I’m missing something and the statement needs to be clarified or additional analysis is needed such as decreases in false-positive-rates on shuffled data with this approach compared to regression.\nAnatomy: I really like the PA-GFP approach, however, this section is confusing as currently written and it did not become clear to me how the findings related to strengthening or refuting parts of the model proposed by the authors. I also believe that this section could be tightened considerably by removing the initial single-cell MapZBrain analysis which doesn’t seem to address any important points that aren’t better made based on the PA-GFP conversion.\nAnatomy 2: The PA-GFP conversions (at least as described) suggest that no neurons cross the mid-hindbrain boundary. How does this chime with the proposed model and the claim that luminance neurons in the tectum feed information to hindbrain neurons?", "reviewer_4": "We co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nIn this study, the authors use an extensive combination of computational and experimental tools to dissect the pathways underlying sensory integration of luminance and motion. The behavioral and neurological bases of each modality in isolation have been characterized in previous studies. The novelty here lies in combining them and uncovering an additive behavioral strategy that is also reflected in brain-wide imaging data. Despite rigorous experiments, the authors do not arrive at a precise mapping for their computational model’s units within the zebrafish brain, nor do they determine how these units are interconnected. Although this may be perceived as a weakness of the study, the authors convincingly apply a diverse set of cutting-edge techniques, generating multiple circuit-level hypotheses, and demonstrating that simple sensory computations can be distributed across many brain regions and cell types, even in a small vertebrate brain. The innovative workflow of this study provides compelling evidence for the distributed nature of neural circuits, from sensory to higher-order networks. This work will be impactful for future efforts to map specific computations at whole-brain scale in vertebrates. We see no major flaw that would compromise the validity of the study, and consider that the study is thorough and meets the standards for publication in Nature Communications. Some minor/moderate comments are provided below to improve readability, some aspects of the discussion, and the interpretation of key results.\nModerate comments:\nIs the proposed model in Fig. 3E the simplest possible algorithm that performs these computations? Given that, after all these experiments, the authors do not arrive at a decisive mapping of the model to the brain, there may be other, potentially more sophisticated models with similar internal dynamics that are implemented by the brain. The possibility of other algorithmic implementations should be discussed.\nIn a similar idea, the activity patterns predicted by the model (Fig. 3F) are quite simple (constant activity during stimulus presentation, activation at stimulus onset and offset, etc). It is not surprising that there are neurons displaying these dynamics, regardless of whether the brain is actually implementing the model or not. Moreover, the presence of neural signals matching the expected model dynamics does not guarantee their causal involvement in the computations. Numerous studies in mice and primates have highlighted how task-related signals can be decoded from most brain regions (for a recent example: https://www.nature.com/articles/s41586-025-09235-0). Complementarily, lesion studies show that many of these regions are in fact unnecessary for task performance. This should be discussed as one of the main limitations of the study, which does not conclude with a clear one-to-one mapping between the model’s units and the larval brain. The search for neural signatures in large-scale neural datasets comes with the ever-present risk of spurious correlations. The authors do a good job of reinforcing their functional imaging results with anatomy and grounded circuit hypotheses, therefore we are not concerned for the study’s validity. However, this is an important discussion element that should be further developed.\nMinor comments:\nSome figure legends are quite long. For instance, Fig. 1A contains many methodological details (size of the arena, camera frame rate, etc) that are not required to interpret the figure panel. In contrast, Fig. 3A is an equivalent panel for the imaging setup, yet has a much shorter description.\nThere are a few narrative shortcuts. Some passages should be better contextualized to improve the accessibility of the manuscript, especially for non-zebrafish researchers. For instance, the expected behavioral response of zebrafish larvae to luminance and motion is not clearly stated. The authors mention that the stimuli “drive directional swimming”, but in which specific direction (i.e. toward light) should be made explicit before presenting the first behavioral results. The ethological significance of these stimuli and behaviors should also be discussed briefly and early on in the manuscript.\nSimilarly, the authors build on previous studies by Bahl and colleagues, without explicitly stating what these studies demonstrated: that higher levels of motion coherence increase the likelihood of swimming in the direction of motion. Without this context, it may be initially unclear what the “psychometric curve” represents for the uninitiated reader when first introduced in Fig. 1E,F.\nRegarding Fig. 1K, I’m not sure why shorter IBIs in congruent stimuli and longer IBS in conflicting stimuli provide evidence for convergent processing. A more mechanistic account of parallel vs convergent processing could be provided first to better interpret those results. In the PCA analysis section, the better separability of congruent stimuli is interpreted as a population-level mechanism for faster reaction times, which does make sense, as better separability may lead to better downstream decoding and faster conversion to motor outputs. While these results are compelling and complement the main analysis, the authors should clarify further how this provides strong evidence for “parallel processing followed by convergent additive integration”.\nIn Fig. 3D, the neurons are compiled across animals, but the number of larvae/experiments is not indicated.\nCould the uneven coverage of the brain in the calcium imaging experiments bias the anatomical mapping of the functional units and the interpretation of the circuit, especially if there is variability across animals ? There is substantial variability in the E:I ratios in Fig. 4D. Could this variability stem from an uneven sampling due to the position of the imaging planes? Perhaps there could be a way, using atlas markers, to estimate how plane positioning may have biased the E:I estimates (though the feasibility of this might be a problem, given that atlas markers are mostly population averages that blur out single-cell information).\nIn Fig. 3F, the open circles are difficult to perceive when not zoomed in on the figure. Moreover, the fact that neurons matching a given model hemisphere are also mostly located within the same brain hemisphere is an interesting observation that is currently not (and should be) addressed in the main text.\n“We obtained a relatively high number of principal components”: this should be rephrased, as PCA does not return an arbitrary number of components. A certain number of components is required to explain a certain fraction of variance.\n“We performed hybridization chain reaction fluorescent in situ hybridization (HCR-FISH) to determine whether functionally identified neurons were (glutamatergic; vglut) or (GABAergic;gad).” The authors should state more clearly how these experiments are carried out in the main text. Larvae are first imaged in vivo, then fixed, then stained, and then re-imaged post hoc, before being computationally registered onto the calcium imaging experiments. This is a long pipeline, and although it is becoming a more common procedure, these experiments are impressive and should be elaborated a bit further in the main text.\n“This balance is likely required for the implementation of the temporal integration properties within each computational unit.” Do the authors say this because the timescale of a computational unit greatly exceeds the synaptic/single-neuron timescale ? If so, then it should be stated more explicitly.\nOn the same topic, the authors could speculate a bit further on why the E:I ratios are so balanced and why the implementation of the algorithm over distributed neuronal populations may be a desirable feature, beyond “shaping output dynamics” and “the implementation of specific temporal dynamics”. In principle, a handful of neurons could have implemented this circuit, yet this is not the case. One possible reason is that larger circuits are more robust to noise. Another is that they may allow for pruning without altering the network output too much (this may be especially relevant in a developing brain).\nTypos: “winter-take-all”, “elevl3”, :)", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point-by-point response to reviewer comments\nReviewer comments highlighted in yellow. Response directly below each point.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this study from the Bahl lab, the authors investigated how zebrafish larvae integrate\nmultiple visual features (motion coherence, luminance level, and changes in luminance)\nwhile making decisions that guide swimming behavior. They first presented a clever set of\nexperiments to carefully dissect visual feature integration in freely swimming zebrafish\nlarvae. Based on the results of these behavioral experiments, they developed a model that\nmimics the animals’ decision-making process. Subsequently, they used two-photon\nmicroscopy to identify neurons across the zebrafish brain that correspond to different nodes\nof the model. They discovered several such neurons with diverse properties in the midbrain\nand hindbrain circuitry. The combined results revealed that zebrafish employ an additive\nstrategy, rather than a winner-takes-all approach, when processing these visual cues. The\nauthors identified three parallel sensory pathways (motion, luminance level, and luminance\nchange) that converge in the anterior hindbrain, which acts as a multi-feature integration\nhub. Interestingly, an earlier study showed that neurons in this anterior hindbrain region are\nrelated to the head direction system, further highlighting the importance of this area. One of\nthe most exciting aspects of this study is the experimental design that enabled photo-\nlabeling of functionally identified groups of neurons. This allowed the authors to determine\nthe morphologies of functionally defined neurons. By combining open neural morphology\ndata with their own functionally labeled anatomical data, the authors refined their working\nmodel, which is presented at the end of the paper and illustrates multi-feature integration of\nvisual stimuli.\nI believe this is an exciting study, paving the way for new approaches to studying brain\nnetworks. I am particularly enthusiastic about the authors’ photo-activation approach, which\nenabled them to identify the morphologies of functionally defined neurons, a non-trivial task.\nThe study has great potential, and I have a few comments and suggestions that I hope will\nhelp improve it:\nComments:\n1. The biggest strength of the manuscript, the photo-labeling of functionally defined neurons,\nis not presented in sufficient detail to assess the quality and clarity of the method. For\nexample, it is unclear how effective this labeling is in targeting the intended neurons. What is\nthe success rate? How confident can we be that the labeled neuron is indeed the targeted\none? As it stands, the presentation lacks sufficient clarity to evaluate potential pitfalls and the\noverall quality of this method.\nThank you for pointing out the strength of the functionally guided photoactivations. We now\nprovide images of 8 example cells in Supplementary Fig. 8c. We also added a breakdown\nof our success rate (single target neuron successfully labeled = 62%) and reasons for failure\n(Supplementary Fig. 8d). We did this quantification manually by carefully comparing pre-\nand post-photactivations. Based on the alignment of neighboring cell bodies before and after\nphotoactivation, we are highly confident that the labeled cell is truly the targeted one. To\nfurther verify the labeling of single neurons, we also generated close-up volume stacks\naround the labeled neurons (Supplementary Movie 4). It is clearly visible that after\nphotoactivation, we only see a single neurite leaving the targeted cell body.\n2. Similarly, the quality of HCR matching to individual functionally imaged neurons is difficult\nto assess. Would it not be better to complement these HCR results with dual-channel\nimaging of calcium signals and neuronal identity using relevant vGlut and Gad1 transgenic\nlines?\nWe agree with the referee that an assessment of the mapping quality would help the reader\nto appreciate the precision of our labeling strategy. We have developed a graphical user\ninterface for an expert annotator to load ANTs-registered datasets. ANTs alone achieves\ngood, but not perfect, matching, requiring this manual inspection step. Our approach further\nhelps as the shapes of cells are largely variable, and HCR FISH distribution in the cytosol is\nbetter quantifiable through a manual analysis. We provide a new Supplementary Fig. 6 to\nshow 8 randomly chosen cells to compare the fixed to functional mapping, as well as the\nneurotransmitter identity labeling. The new figure also shows a screenshot of the GUI\nillustrating the rigor of our annotation process. This display shows that we can make\nneurotransmitter assignments with very high confidence to functionally characterized cells.\nWe agree that the dual-channel imaging using vGlut and Gad1 transgenic lines is a good idea,\nand it may simplify mapping between live- and fixed-imaging stacks. However, we only have\ngad1b:dsRed and vglut2a:dsRed lines (Satou et al. 2013) in our lab. This means, in\ncombination with GCaMP imaging (green), we would only be able to assign excitatory or\ninhibitory labels to functionally identified cells per fish, but not both within the same experiment.\nWe would require one of these transgenic lines expressing fluorophores of different colors\n(such as blue), which, to our knowledge, do not exist. In addition, with HCR FISH, we can label\nmultiple variants of gad (gad1a, gad1b, gad2) and vglut (vglut2a, vglut2b), enhancing\nconfidence about the excitatory/inhibitory labels.\n3. The statement “Based on our previous structure-to-function analyses of motion processing\nneurons in the anterior hindbrain, we model these connections as likely inhibitory” seems to\nrely on a limited number of data points, expanding this data could be important. Is there no\nvariability in the anatomy of functionally identified neurons? Are they truly so similar?\nThanks for pointing out that this needs further clarification. In this paper, we have performed\nthe neurotransmitter labeling after functional imaging without morphological analysis (Fig. 4).\nThe anatomical analyses (Fig. 5) were done after functional imaging, however, without HCR\nFISH. Therefore, we cannot fully constrain the neurotransmitter labels within specific\nanatomical projection groups, as these results come from different experiments and fish.\nHowever, in our recent paper (Boulanger-Weill et al. 2025, BioRxiv), we performed correlated\nlight and electron microscopy in the anterior hindbrain in fish with inhibitory neurons\nfluorescently labeled. We could thus obtain information about the neurotransmitter identity as\na function of anatomical projection pattern at the same time. 26 out of 29 reconstructed motion-\nresponsive cells with contralateral projections were Gad1b+. Based on the result of Boulanger-\nWeill et al. 2025, we modeled the projections in this current paper as likely inhibitory. We hope\nthe referee agrees that the size of this dataset provides a sufficient number of data points for\nthe statement. To better explain our reasoning, we have updated and refined the main text of\nthe manuscript accordingly.\nOur reconstructions also show that there is indeed cell-to-cell variability within functional types,\nfor example, for the motion and multifeature integrators. For each cell type, we grouped those\ndifferent anatomies into ‘anterior’, ‘contralateral’, and ‘local’, based on where the projections\ngo (Fig. 5d). We also added the number of neurons in each of the subplots of Fig. 5d to the\nfigure legend for clarification.\n4. The anatomical descriptions are very zebrafish-centric and difficult to relate to general\nconcepts of the vertebrate brain. Could the authors expand the discussion to relate their\nfindings to what is known about vertebrate hindbrain circuits?\nThanks for pointing this out. We added further information comparing our work to other\nvertebrate species (see the paragraph starting at line 603). Please note that we have limited\nthese additions to a minimum, as per the request of Reviewer #2 (point 3), not to overreach in\ncross-species comparisons, and not to over-interpret these analogies as direct homologies.\n5. The authors state: “Furthermore, we conclude that motion and luminance cues jointly\ncontrol swim initiation, leading to delayed responses in conflicting conditions.” I could not find\nthe specific data or results that support this conclusion. Could you please clarify?\nThank you for pointing out that this has not been clear enough. We based our conclusion on\nthe different interbout intervals for congruent stimuli compared to conflicting stimuli (Fig. 1k).\nWe better explain our reasoning in (see lines 183–187) in our updated manuscript.\n6. The paragraph starting at line 211 is difficult to read and lacks sufficient figure citations.\nWe rewrote the paragraph to be clearer and added more relevant figure citations using\nmore precise panel labeling.\n7. The authors’ results highlight the anterior hindbrain, a brain region recently shown to\ncontain the head direction system in zebrafish. It would be important for the authors to\ndiscuss how their findings align with this earlier work.\nWe thank the referee for pointing out that the anterior hindbrain contains neurons related\nto a navigational head direction system. Our and others' findings indicate that this region\nalso represents multi-feature stimuli. These results together align well with work in the\nDrosophila central complex, which integrates multiple sensory cues across modalities.\nWe are now discussing this important point in our updated manuscript (the paragraph\nstarting at line 595).\n8. The authors discuss visual responses in the habenula and cite a paper from the Julin Du\nlab, but they seem to have missed the earlier paper by Dreosti et al. (2014), which first\nshowed such visual responses. Please consider including this reference.\nWe included the missing reference.\n9. The y-axis in Figure 1I is not well defined.\nWe added the unit and updated the plot.\n10. Figure legends need clearer explanations, and some color codes are difficult to interpret.\nWe adjusted our figure legends to be clearer and more concise (as also requested by\nReviewers #4 and #5, point 3). We improved the color code in Fig. 2 to reduce the\nnumber of shades of blue, making it easier to interpret the figure. If the referee agrees,\nwe would like to keep the coloring style in the Figs. 3–5. They are color-blind friendly\narrangements without added unnecessary complexity.\n11. The statements “We further find that the computation of lateral luminance change\ndetection happens locally in the optic tectum” and “First, an excitatory connection of motion\nintegrators onto multifeatured neurons without clear compartmentalization. Second, an\ninhibitory ipsilateral connection from lateral luminance level integrator cells onto multifeature\nneurons” are difficult to interpret. What specific datasets support these claims?\nThank you for pointing out that these sentences were not clear enough. They both\ndescribe how we refine our behaviorally constrained model. We rephrased this section\nand added references to the corresponding figure panels to better justify our\nargumentation. See paragraph starting at line 496.\n12. Similarly, the claims about dendritic computations on line 524 are not substantiated with\ndata. I recommend toning down these statements.\nThanks, and we agree. We further toned down the statement, now on line 519.\nReviewer #2 (Remarks to the Author):\nVisuomotor decision-making through multifeature convergence in the larval zebrafish\nhindbrain\nThis study investigates the neural basis of multifeature visual integration in larval zebrafish.\nThe authors combine precise behavioural assays with computational modeling to provide\nconvincing evidence that zebrafish employ an additive algorithm to integrate three visual\ncues during sensorimotor decisions - coherent motion, steady-state luminance levels, and\ntransient luminance changes. Using brain-wide two-photon calcium imaging, they map these\ncomputations to distinct neural populations, demonstrating motion and multifeature\nintegration in the anterior hindbrain and luminance processing in the optic tectum.\nComplementary analyses of neurotransmitter identity (via HCR-FISH) and single-neuron\nmorphology (via pa-GFP photoactivation) support a circuit model featuring parallel pathways\nthat converge additively. The paper provides a biologically plausible framework linking\nbehaviour to neural implementation, with broad implications for sensory integration across\nvertebrates. The work extends single-feature studies (e.g., motion in Bahl & Engert, Nat.\nNeurosci. 2020 [ref. 17]; luminance in Wolf et al., Nat. Commun. 2017 [ref. 22]) to\nmultifeature contexts. Overall, I think that this is a very interesting paper that will have impact\nfor future work across animal models used to investigate the neural basis of behaviour.\nSignificance: This advances our understanding of how vertebrate brains resolve sensory\nconflicts via parallel processing, with the anterior hindbrain emerging as a versatile\nintegration hub. It addresses a core question in decision neuroscience—additive vs.\ncompetitive algorithms—while providing a blueprint for linking behaviour to circuits in\ncompact brains. The multi-level analysis of this problem highlights the advantages of the\nlarval zebrafish as a model. I can’t think of a study using mice that similalrly integrates\nbehaviour, algorithmic modelling, brain-wide recording of activity (!) and pathway mapping.\nAn excellent piece of work.\nMinor concerns and suggestions\nThe manuscript has been prepared to a very high standard and documents the work in\ndetail. The strongest parts of the manuscript are the behavioural analysis, the algorithmic\nmodelling, and the survey and analysis of neural responses. The latter sections that attempt\nto link neural morphology/transmitter type to the circuit model were less satisfying overall, in\nlarge part because they lacked any experimental tests (e.g by manipulating activity). But I\ndon’t think it would be reasonable to ask for this to be added to this manuscript (although it\nmight warrant a mention in the Discussion).\nWe thank the referee for this encouraging and positive overall assessment. We\nextended our discussion on future work with a focus on the need for causal\nmanipulations, providing an example of what such manipulations may test for (line 646).\nThe manuscript is already very strong, but a few clarifications would polish it further:\n1. Figure 2 and model details: The luminance change pathway's high weight but modest\nsilencing effect is well-explained, but quantifying its transient contribution (e.g., via integrated\nactivity over stimulus edges) could strengthen the intuition. Consider adding a supplemental\nsimulation showing pathway contributions across time.\nWe appreciate your idea on how to strengthen the intuition of our result. We added a panel\nto Supplementary Fig. 2c, which demonstrates the simulated pathway contribution across\ntime.\n2. Neural classification: Logical statements are effective, but the thresholds (e.g., ΔdF/F0 >\n0.1) feel somewhat arbitrary—briefly justify them in Methods (e.g., based on noise floors) or\nvia a ROC curve in supplements. The winner-takes-all cells (Extended Data Fig. S3D) are\nintriguing; a sentence speculating on their recruitment (e.g., under time pressure) could tie to\nthe discussion's flexibility theme.\nWe agree that thresholds add an unavoidable bit of arbitrariness to functional imaging studies.\nWe added a justification based on the noise levels in our data to the Methods (lines 973–\n977), indicating that threshold values are more than 2 standard deviations above baseline\nnoise floors. We also added a sentence to our discussion speculating on the advantage of\nrecruiting winner-takes-all cells under increased time pressure (lines 622-625).\n3. General: There is a tendency to repetition in the manuscript, especially in the Discussion.\nReadability could be improved by cutting some of this and making the Discussion more\nfocused. For instance, it feels like there is some overreach in cross-species comparisons.\nThe claim of “conserved principles” ignores algorithmic differences—mammalian\nmultisensory integration often involves divisive normalization (Ohshiro et al., Nat. Neurosci.\n2011 [ref. 57]), not just addition. The authors’ mention of sensory processing disorders feels\ncursory to this reviewer, although this is a matter of taste.\nLeon Lagnado\nThanks for pointing this out. We made our discussion more focused and improved readability.\nGiven the request of Reviewer #1 (point 4), we have refined the text where we compare our\nfindings with work done in other species, while limiting overreach in cross-species\ncomparisons (the paragraph starting at line 603). Following the referee's recommendation, we\nnow also mention divisive normalization as an alternative integration strategy (line 608–610).\nReviewer #3 (Remarks to the Author):\nThe paper “Visuomotor decision-making through multifeatured convergence in the larval\nzebrafish hindbrain” by Slangewal et al., describes behavioral and functional responses\nacross the zebrafish brain to reorientation-inducing visual stimuli. Given previous literature\non motion integration and phototaxis, the data obtained in this manuscript aren’t necessarily\nsurprising. However, the manuscript combines responses to luminance differences and\nlateral motion in a clever way. The central claim of the paper is that luminance differences\nand motion direction are processed in parallel pathways. Given the proposed architecture of\nvisual processing parallel processing of features is not surprising. However, I am not sure if\nthe presented data supports the term “parallel pathways” well enough, see below. While\nexperiments and analysis are well done overall, I have strong concerns with some of the\ninterpretations which need to be addressed before publication.\n-Major concerns-\nA major finding according to the authors is the integration of information from two parallel\n“pathways” a motion pathway and a luminance pathway. Given visual system architecture\nand responses, parallel processing is not surprising. However, the term “parallel pathways”\nsuggests that information is kept separate across the circuits until it hits the “multifeature\nintegrators.” The experiments conducted by the authors are not sufficient to draw this\nconclusion. The experiments demonstrate that at the level of behavior, there is no\nmultiplicative interaction while the imaging experiments suggest a separation of the\npathways, except the “multifeature integrators.” However, the experiments cannot show that\nthese multifeature integrators are in fact at the end of pathways rather than in the middle,\netc. In the end, a well-supported conclusion is that the information adds linearly and that\nthere are neural populations that separately process the information as well as neural\npopulations that might integrate the information (more on that below). I would therefore\nsuggest the authors speak of “parallel processing” and “linear addition” instead of “parallel\npathways.”\nThank you for pointing out that ‘parallel processing’ and ‘linear addition’ better reflect our\nfindings. We have adjusted our wording throughout our updated manuscript, and now only\nuse the suggested terms and specify pathways as ‘processing pathways’ or ‘computational\npathways’.\nPrevious work has demonstrated beautifully that neurons in the anterior hindbrain integrate\nlateral motion cues. A hallmark of this integration is that motion coherence affects the speed\nof integration but not the reachable level. In fact, this is critical information to conclude that a\nneural population integrates information. Simply showing that activity rises slowly is\ninsufficient, especially with calcium imaging. It is therefore misleading to name the yellow\npopulation (top right) in Figure 3F “Luminance integrators” despite their match to model\npredictions for one luminance level. The authors will need to show that the activity slope\nwithin these neurons depends on luminance contrast while the level reached does not.\nAbsent that, the neurons should be given a name that reflects that the neurons show\nsustained responses to luminance differences. This is the only conclusion that can be drawn\ngiven the current data.\nIn a similar vein, I don’t think that the blue population in the same figure (bottom right) can be\nconfidently labeled “Multifeature integrators.” There are clear differences between the\nresponses and the predictions which highlights an overall problem: How is it decided that the\nmatch “is good enough” to warrant the name given by the model?\nWe thank the reviewer for helping us to clarify the naming of model nodes in our manuscript.\nRegarding the luminance integrators, we have performed a new imaging experiment, following\nthe referee's advice. Our new data shows slower integration for a weaker lateral luminance\ncue, while reaching similar activity levels. Therefore – and we hope the referee agrees – we\nfeel confident that these cells can indeed be labeled as luminance integrators. We added the\nresults to Supplementary Fig. 3b,c.\nAlso, we appreciate the referee's comment regarding the multifeature integrators. Whether a\nmatch in imaging data is considered ‘good enough’ to represent the model node is determined\nby our logical statements (see also point 6). For the multifeature neurons, the statements\nensure that each selected neuron responds to all three features: motion, luminance, and\nchanges in luminance, as well as the combinations of these features. Given these criteria, we\nare confident to use the label ‘multifeature integrator’.\nNotably, our logical statement approach does not precisely fit model traces to neural data over\ntime, but rather focuses on key features of the response. Therefore, residual differences\nbetween model response dynamics and experimental data are expected.\nThe most obvious difference between the model and the data is the decreasing response\nbelow baseline of motion integrators and multifeature integrators in the left hemisphere when\nmotion is presented to the right, and vice versa for the right hemisphere. A possible\nexplanation is the existence of contralateral mutual inhibition of motion integrators and/or\nmultifeature integrators. Our anatomical analyses (Fig. 5) identify contralateral projections\nbetween motion integrators across hemispheres as well as contralateral projections of\nmultifeature integrators across hemispheres (Fig. 5f). These projections could explain the\nmismatch in dynamics in Fig. 3f. We describe these issues now in our updated manuscript\n(lines 521–527 and 616–619).\nGiven the current analysis, this statement is misleading. To properly characterize the\npresumed “manifold”, the dimensionality of the data needs to be considered. Given the\nfraction of explained variance, it has to be assumed that this dimensionality is larger than 30\nas 30 components only explain a little more than 50% of the variance. Statements about\northogonality will need to consider the full dimensionality. What the authors find in Figure S4\nis therefore not a feature of the data but a feature of the analysis. This section does not add\ninformation while being used to draw a misleading conclusion - the section title suggests the\nexistence of a “low-dimensional space” and “orthogonal processing.” I also do not believe\nthat this information is important for the overall work - the authors already show a separation\nof encoding on the individual neuron level. Considering the model structure, this seems to be\nthe more important result.\nWe agree that using terms like orthogonal processing was not appropriately phrased, in\nparticular given the dimensionality of our data. We have adjusted the title of this section and\nshortened the text to focus our findings on the separability of congruent vs. conflicting stimuli\nin low-dimensional neural space.\nWe think that including an analysis framework in reduced-dimensional neural space adds\nvalue to our manuscript. Such unsupervised methods are commonly used in neuroscience\nand allow one to explore the brain dynamics without a model-guided strategy. Indeed, our\nresults on the separability of different stimulus classes nicely match our findings from the\nbehavior: For congruent stimulus cases, we have found that behavioral response delays are\nshorter than for conflicting configurations (Fig. 1K), which is reflected by distinct neural\ndynamics in low-dimensional space (Supplementary Fig. 5d). For these reasons, we would\nkindly ask to keep this supplementary information, after the mentioned adjustments, if the\nreferee agrees.\nSince a lot of the features discovered by the authors are predicted by prior work in the visual\nsystem, there should be a broader discussion on properties of visual responses and how\nthese relate to the findings. The discussion instead seems to unnecessarily focus on\nhighlighting perceived advances of the authors’ approach which may or may not be\nwarranted.\nWe more rigorously link our results to prior work in the visual system through an improved\ndiscussion section. We have also adjusted our text to tone down statements related to the\nconceptual and methodological advances of our study.\n-Minor points-\n“Winner take-all responses”: The definition of the authors is not sufficient to label these types\nof neurons as “winner take-all.” The neurons the authors describe with this name are just\nlinear adders (with negative sign), while “winner take-all” cells should respond to the more\nimportant feature, either in a constant or stochastic manner.\nThank you for catching this mistake, and we apologize for our incorrect explanation. The\nwinner-takes-all model – two types of neurons connected through mutual inhibition –\n(Supplementary Fig. 4d) behaves as the referee points out: When driven by similar input,\none of them will stochastically take over, inhibiting the other one. We have now updated the\ntext (lines 369–372).\nNeuron identification by logical statements: This is a clever idea. However, the fact that\nfewer neurons are identified compared with regression is not enough to conclude that the\napproach is better or worse. Maybe I’m missing something and the statement needs to be\nclarified or additional analysis is needed such as decreases in false-positive-rates on\nshuffled data with this approach compared to regression.\nThank you for pointing out that our statement requires further validation. We have now\nperformed additional new analyses to address this concern. Specifically, we used our model\n(Fig. 3e) to generate neural data for two functional cell types, one with transient dynamics\n(luminance change detectors) and one with constant dynamics (luminance integrators). We\nthen added different amounts of Gaussian noise to those traces. We then checked to what\ndegree our logical statements and linear regression analysis would label neurons correctly.\nQuantification of the percentage of correctly classified neurons showed that our logical\nstatement strategy does indeed perform better than linear regression for both cell types, in\nparticular when signal-to-noise levels get lower. We added this additional perspective to\nSupplementary Fig. 4e–h and now explain this approach in detail in the text (lines 354-366)\nand in the Methods under ‘Synthetic data simulation’.\nWe also added the false-positive rate on shuffled data for linear regression (Supplementary\nFig. 4b), which is similarly low to the false-positive rate for our logical-statement-based\napproach.\nAnatomy: I really like the PA-GFP approach, however, this section is confusing as currently\nwritten and it did not become clear to me how the findings related to strengthening or\nrefuting parts of the model proposed by the authors. I also believe that this section could be\ntightened considerably by removing the initial single-cell MapZBrain analysis which doesn’t\nseem to address any important points that aren’t better made based on the PA-GFP\nconversion.\nWe largely restructured and rewrote this section of the results to improve its clarity and\nhighlight how our findings relate to the updates of the model. See text from line 496 onwards.\nThank you for providing a suggestion on how to tighten this section. We think the mapzebrain\nanalysis provides important context to our study, considerably narrowing down the possible\nprojection patterns and neural diversity from the tectum to the anterior hindbrain. Without such\nknowledge, it would be difficult to know the order of magnitude of neurons needed to be\ngenerated through the time-consuming function-guided PA-GFP experiments. Moreover, this\npublic database contains an increasingly large number of reconstructed cells, which will further\nfacilitate hypothesis-driven circuit neuroscience over the next years. Showing that the\nneuronal morphologies downloaded from the database match the morphologies identified\nthrough photoactivation validates both the public platform as well as our method. We\nshortened our text related to this part and moved mapzebrain-based anatomies to\nSupplementary Fig. 8a,b.\nAnatomy 2: The PA-GFP conversions (at least as described) suggest that no neurons cross\nt cross\nthe mid-hindbrain boundary. How does this chime with the proposed model and the claim\nthat luminance neurons in the tectum feed information to hindbrain neurons?\nIndeed, the referee is right about that we did not find any mid-hindbrain crossings. The\nluminance neurons in the tectum (which is part of the midbrain) reach the multifeature neurons\nof the anterior hindbrain through two boundaries. The tectal neurons project ventrally and\ncross the midbrain-forebrain boundary. The multifeature neurons project ventrally and\nanteriorly and cross the hindbrain-forebrain boundary. Both neuron types then converge in the\nventral forebrain. For an illustration of the brain region arrangement, please see Fig. 3b. We\nhave improved the explanation of these structural features in our updated results section (lines\n489–495).\nReviewer #4 (Remarks to the Author):\nWe co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nIn this study, the authors use an extensive combination of computational and experimental\ntools to dissect the pathways underlying sensory integration of luminance and motion. The\nbehavioral and neurological bases of each modality in isolation have been characterized in\nprevious studies. The novelty here lies in combining them and uncovering an additive\nbehavioral strategy that is also reflected in brain-wide imaging data. Despite rigorous\nexperiments, the authors do not arrive at a precise mapping for their computational model’s\nunits within the zebrafish brain, nor do they determine how these units are interconnected.\nAlthough this may be perceived as a weakness of the study, the authors convincingly apply\na diverse set of cutting-edge techniques, generating multiple circuit-level hypotheses, and\ndemonstrating that simple sensory computations can be distributed across many brain\nregions and cell types, even in a small vertebrate brain. The innovative workflow of this study\nprovides compelling evidence for the distributed nature of neural circuits, from sensory to\nhigher-order networks. This work will be impactful for future efforts to map specific\ncomputations at whole-brain scale in vertebrates. We see no major flaw that would\ncompromise the validity of the study, and consider that the study is thorough and meets the\nstandards for publication in Nature Communications. Some minor/moderate comments are\nprovided below to improve readability, some aspects of the discussion, and the interpretation\nof key results.\nModerate comments:\nIs the proposed model in Fig. 3E the simplest possible algorithm that performs these\ncomputations? Given that, after all these experiments, the authors do not arrive at a decisive\nmapping of the model to the brain, there may be other, potentially more sophisticated\nmodels with similar internal dynamics that are implemented by the brain. The possibility of\nother algorithmic implementations should be discussed.\nFrom the behavior, we see that there are three fundamental components: Animals respond to\nmotion cues, to steady-state lateral luminance cues, as well as to the changes in luminance.\nAs such, we need three components in our model to capture key aspects of the behavior. To\nour knowledge, no simpler model can capture our results. To explicitly probe the contributions,\nwe algorithmically silenced individual pathway components, identifying that each of them is\nimportant to describe aspects of the behavior (Fig. 2h).\nWe agree with the referee that more complex models than the proposed one are likely to be\nimplemented by the brain. In more naturalistic settings, animals need to cope with other\nchallenges, they flexibly adapt across development, and they socially interact. All these\ncomponents would need to be iteratively explored and added to the model to increase its\ncomplexity. Furthermore, apparently even simple computations, such as taking the temporal\nderivative of a luminance signal, can involve sophisticated temporal processing, which is the\nreason why identified computational representations are more complex than simple unit-to-\nneuron mapping.\nWe think the minimalist algorithmic modelling is an important starting point for the dissection\nof neural circuits. Indeed, the model allowed us to develop precise hypotheses to search for\ncomputational nodes in the brain, which we then identified. Analyzing anatomical details of\nthese neurons then enabled us to expand and refine our model (Fig. 5f). This shows that a\nbehavioral model-based circuit dissection loop can iteratively lead to a better understanding\nof the functioning of neural circuits and should provide a key strategy in neuroscience.\nWe have addressed these important points in the discussion (paragraph starting at line 613).\nIn a similar idea, the activity patterns predicted by the model (Fig. 3F) are quite simple\n(constant activity during stimulus presentation, activation at stimulus onset and offset, etc).\nIt is not surprising that there are neurons displaying these dynamics, regardless of whether\nthe brain is actually implementing the model or not. Moreover, the presence of neural signals\nmatching the expected model dynamics does not guarantee their causal involvement in the\ncomputations. Numerous studies in mice and primates have highlighted how task-related\nsignals can be decoded from most brain regions (for a recent\nexample: https://www.nature.com/articles/s41586-025-09235-0). Complementarily, lesion\nstudies show that many of these regions are in fact unnecessary for task performance. This\nshould be discussed as one of the main limitations of the study, which does not conclude\nwith a clear one-to-one mapping between the model’s units and the larval brain. The search\nfor neural signatures in large-scale neural datasets comes with the ever-present risk of\nspurious correlations. The authors do a good job of reinforcing their functional imaging\nresults with anatomy and grounded circuit hypotheses, therefore we are not concerned for\nthe study’s validity. However, this is an important discussion element that should be further\ndeveloped.\nWe agree that the presence of neural signals matching the expected model dynamics does\nnot guarantee their causal involvement in behavior. We further highlighted the need for causal\nmanipulations in future studies to complement this work (lines 645–651).\nWe also agree with the referee that searching for neural signatures across large-scale\ndatasets bears the risk of false-positive correlations. We would like to point out that we have\nprobed our analyses using control configurations where stimuli were replaced with 0 %\ncoherence on a homogenous background (Supplementary Fig. 4b,c). Here, even though we\nonly take data without directional stimuli, we do find a few cells matching our logical statements\nand functional regressors; however, these cells were much fewer in number without spatial\narrangements. Therefore, we conclude that the majority of the identified cells using our visual\nstimuli are real representations of the explored computation. We strengthened this point in the\nrespective result section of our updated manuscript (lines 361–366).\nMinor comments:\nSome figure legends are quite long. For instance, Fig. 1A contains many methodological\ndetails (size of the arena, camera frame rate, etc) that are not required to interpret the figure\npanel. In contrast, Fig. 3A is an equivalent panel for the imaging setup, yet has a much\nshorter description.\nThank you for your comment. We adjusted the figure legends (also based on the\ncomment of Reviewer #1, point 10) and removed unnecessary methodological details.\nThere are a few narrative shortcuts. Some passages should be better contextualized to\nimprove the accessibility of the manuscript, especially for non-zebrafish researchers. For\ninstance, the expected behavioral response of zebrafish larvae to luminance and motion is\nnot clearly stated. The authors mention that the stimuli “drive directional swimming”, but in\nwhich specific direction (i.e. toward light) should be made explicit before presenting the first\nbehavioral results. The ethological significance of these stimuli and behaviors should also be\ndiscussed briefly and early on in the manuscript.\nThanks for pointing this out and providing explicit examples. We added clarification for these\nbehaviors and their ethological relevance to the introduction (lines 70–72) as well as to the\nresults part (lines 147–151).\nSimilarly, the authors build on previous studies by Bahl and colleagues, without explicitly\nstating what these studies demonstrated: that higher levels of motion coherence increase\nthe likelihood of swimming in the direction of motion. Without this context, it may be initially\nunclear what the “psychometric curve” represents for the uninitiated reader when first\nintroduced in Fig. 1E,F.\nThank you, we added further context and explained expectations (lines 147–151).\nRegarding Fig. 1K, I’m not sure why shorter IBIs in congruent stimuli and longer IBS in\nconflicting stimuli provide evidence for convergent processing. A more mechanistic account\nof parallel vs convergent processing could be provided first to better interpret those results.\nIn the PCA analysis section, the better separability of congruent stimuli is interpreted as a\npopulation-level mechanism for faster reaction times, which does make sense, as better\nseparability may lead to better downstream decoding and faster conversion to motor outputs.\nWhile these results are compelling and complement the main analysis, the authors should\nclarify further how this provides strong evidence for “parallel processing followed by\nconvergent additive integration”.\nWe adjusted the text to clarify our reasoning regarding Fig. 1K (lines 183–187). For the\ncomment on our PCA analysis, please see our response to reviewer #3, point 3. Following\nreviewer #3’s advice, we largely rewrote the section for the PCA-based analysis.\nIn Fig. 3D, the neurons are compiled across animals, but the number of larvae/experiments\nis not indicated.\nWe clarified in the figure legend of Fig. 3 that the N=15 fish corresponds to the data across\nthe entire figure.\nCould the uneven coverage of the brain in the calcium imaging experiments bias the\nanatomical mapping of the functional units and the interpretation of the circuit, especially if\nthere is variability across animals ? There is substantial variability in the E:I ratios in Fig. 4D.\nCould this variability stem from an uneven sampling due to the position of the imaging\nplanes? Perhaps there could be a way, using atlas markers, to estimate how plane\npositioning may have biased the E:I estimates (though the feasibility of this might be a\nproblem, given that atlas markers are mostly population averages that blur out single-cell\ninformation).\nWe thank the referee for bringing this to our attention and for the constructive feedback. We\nnow better illustrate the brain-wide distribution of functionally classified cell types. To this end,\nwe improved the text to focus the reader on Supplementary Fig. 3a, in which we display brain\nregion and cell-number normalized ratios. This plot thus corrects for the uneven coverage of\nour imaging as well as brain-region size differences. We argue for chosen regions for\nanatomical mapping based on this plot as well as based on general literature references.\nWe are confident that the variability in the E:I ratios in Fig. 4d is not a result of uneven\ncoverage bias. Because in this experiment, we ensured near-identical imaging conditions of\nexactly the same volume across animals. We added a sentence to the methods to clarify the\nimaging configuration in this experiment (lines 1083–1087).\nIn Fig. 3F, the open circles are difficult to perceive when not zoomed in on the figure.\nMoreover, the fact that neurons matching a given model hemisphere are also mostly located\nwithin the same brain hemisphere is an interesting observation that is currently not (and\nshould be) addressed in the main text.\nWe adjusted the style of the open circles to make it easier to distinguish the open vs filled\ncircles. We adjusted this in all brain maps in Fig. 3f-g, Fig. 4d, Supplementary Fig 3b,\nSupplementary Fig. 4a-c, and Supplementary Fig. 7a. We now also highlight the\nhemispheric symmetry (lines 342-344).\n“We obtained a relatively high number of principal components”: this should be rephrased,\nas PCA does not return an arbitrary number of components. A certain number of components\nis required to explain a certain fraction of variance.\nTrue, thank you for finding this mistake. We rephrased the sentence to ‘We found that 30\nprincipal components were necessary to explain 53% of the total variance’.\n“We performed hybridization chain reaction fluorescent in situ hybridization (HCR-FISH) to\ndetermine whether functionally identified neurons were (glutamatergic; vglut) or\n(GABAergic;gad).” The authors should state more clearly how these experiments are carried\nout in the main text. Larvae are first imaged in vivo, then fixed, then stained, and then re-\nimaged post hoc, before being computationally registered onto the calcium imaging\nexperiments. This is a long pipeline, and although it is becoming a more common procedure,\nthese experiments are impressive and should be elaborated a bit further in the main text.\nThank you for the recognition of our experimental efforts. We now better explain the pipeline\nin the main text (lines 414–419). Moreover, we added a new Supplementary Fig. 6, for the\ndetailed experimental and computational registration procedures, as well as displaying the\nquality of mapping results.\n“This balance is likely required for the implementation of the temporal integration properties\nwithin each computational unit.” Do the authors say this because the timescale of a\ncomputational unit greatly exceeds the synaptic/single-neuron timescale ? If so, then it\nshould be stated more explicitly.\nYes, exactly, we made this sentence more explicit (lines 530–531).\nOn the same topic, the authors could speculate a bit further on why the E:I ratios are so\nbalanced and why the implementation of the algorithm over distributed neuronal populations\nmay be a desirable feature, beyond “shaping output dynamics” and “the implementation of\nspecific temporal dynamics”. In principle, a handful of neurons could have implemented this\ncircuit, yet this is not the case. One possible reason is that larger circuits are more robust to\nnoise. Another is that they may allow for pruning without altering the network output too\nmuch (this may be especially relevant in a developing brain).\nThank you for the suggestions on these important discussion points. Robustness, temporal\nintegration, and developmental stability are indeed key features of neural networks, and we\nhave now improved our discussion of these aspects. (lines 631–636).\nTypos: “winter-take-all”, “elevl3”, :)\nThank you for catching those! ;-)\nReviewer #4 (Remarks on code availability):\nWe have not run the Python code to reproduce the results, but inspected the files and the\ncode is well-organized, with each figure and supplementary figure supported by its own\nscript. However, the repository is minimally documented, and we recommend the authors\nimprove its presentation and accessibility if the paper is to be accepted and published.\nWe thank the referees for acknowledging our efforts. We have further improved our code\ndocumentation, which should largely improve readability.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #5 (Remarks on code availability):\nI have not run the Python code to reproduce the results, but I inspected the files and the\ncode is well-organized, with each figure and supplementary figure supported by its own\nscript. However, the repository is minimally documented, and I recommend the authors\nimprove its presentation and accessibility if the paper is to be accepted and published.\nWe thank the referees for acknowledging our efforts. We have further improved our code\ndocumentation, which should largely improve readability."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors successfully addressed my comments. I also think that they have done substantial amount of work to address other reviewers comments successfully too. I recommend the publication of this manuscript at Nature Communications.", "reviewer_2": "The manuscript has been significantly improved and I judge it to be of a very high standard and of high interest.", "reviewer_3": "I thank the authors for engaging with my comments and focusing the conclusions of the paper on aspects that are fully supported by the data. I think that the paper overall presents a very nice dissection of stimulus features triggering visually guided behaviors, and that it should be published in its current form.", "reviewer_4": "the authors have addressed each of our points through appropriate textual revisions and additional figures. We have no remaining concerns and consider the revised manuscript significantly improved.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter