Title: Connectome-Based Modelling Reveals Orientation Maps in the Drosophila Optic Lobe
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computational Neuroscience

Drosophila Visual System

Orientation Maps

Leaky Integrate-and-Fire Model

Connectome Simulation
Abstract: The ability to extract oriented edges from visual input is a core computation across animal vision systems. Orientation maps, long associated with the layered architecture of the mammalian visual cortex, systematically organise neurons by their preferred edge orientation. Despite lacking cortical structures, the *Drosophila melanogaster* brain contains feature-selective neurons and exhibits complex visual detection capacity, raising the question of whether map-like vision representations can emerge without cortical infrastructure. We integrate a complete fruit fly brain connectome with biologically grounded spiking neuron models to simulate neuroprocessing in the fly visual system. By driving the network with oriented stimuli and analysing downstream responses, we show that coherent orientation maps can emerge from purely connectome-constrained dynamics. These results suggest that species of independent origin could evolve similar visual structures.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors demonstrate the topographic organization of orientation selectivity in the optic lobe of Drosophila melanogaster. This result is achieved by simulating leaky integrate-and-fire neurons connected through the complete connectome of the Drosophila brain. The analysis of the model reveals that different brain areas exhibit varying proportions of neurons well-fitted to orientation selectivity, and these proportions are well-correlated with the selectivity predicted by dendritic structure alone.\n\nSTRENGTHS AND WEAKNESSES:\nThe primary strength of the paper is the novelty of its results, as it shows for the first time the presence of a smooth topographic map of orientation selectivity in Drosophila. As noted in the limitations section, the main limitation and weakness is the inability to validate these results using in vivo data.\n\nMinor:\n- Consider using a Von Mises distribution instead of a Gaussian fit for computing orientation selectivity.\n- When analyzing the smoothness of the topographic map, a control by bootstrap resampling could be used to check for significance.\n\nQUESTIONS:\nOne function of topographic maps is to connect neighboring neurons with similar selectivities (see, e.g., doi:10.1007/s00429-022-02455-4). Is this something that may exist in these highly recurrent networks, particularly in the optic lobe?\n\nA possible confound is that the map is organized mainly to detect motion, and due to the aperture problem, orientation and direction are perpendicular to each other. Is the orientation map superimposed on a direction map?\n\nThere seems to be a cardinal bias in Dm15; how do you interpret this?\n\nLIMITATIONS:\nOne limitation is that the results depend on the correctness of the modeling, and dendritic structure alone may be sufficient to predict most features. For instance, the paper shows that most T4 and T5 neurons had firing rates close to zero, but it is unclear whether this is a simulation artifact or a real biological fact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study examines the presence of orientation maps in the fly visual system, addressing whether functional organization principles analogous to those in mammalian visual cortex can emerge in invertebrate neural architectures lacking laminated cortical structures. The authors leverage the FAFB connectomic dataset and LIF modelling to characterize orientation selectivity across the fly's visual processing pathway.\n\nKey findings:\n1. Neurons within the medulla exhibit robust orientation tuning.\n2. Preferred orientations are not distributed randomly but form smooth, continuous gradients with pinwheel-like singularities in both Dm and Pm layers.\n3. Column-wise analysis reveals vertically organised orientation columns spanning multiple sublayers. The neurons within the dm and pm layers suggest higher consistency in orientation tuning.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- Whether map-like visual representations require cortical structure is an open and interesting question in neuroscience.\n- The flow of the paper is logical and well-structured.\n- This work leverages the FAFB connectome and LIF model to simulate and analyze orientation selectivity in the fly visual system, which seems to be a feasible approach to address the research question.\n- The authors provide a comprehensive analysis of orientation maps across the fly's visual processing pathway.\n\n## Weaknesses\n- The `homogeneity neuronal property assumption` in the model setting can be a limitation, as a hidden assumption behind all their results and analysis is that the structural information used in the computational model without training can infer functional properties. However, I think the `homogeneity neuronal property assumption` is too strong in this scenario.\n\nQUESTIONS:\nQuestions:\n1. See the `weaknesses`.\n2. How many neurons were used in the LIF model? The authors should clarify the number of neurons used in the LIF model and how they were selected.\n3. Regarding `Appendix B`, lines 907-909, why does the fitting of `A_x` and `B_x` result in integer values?\n4. How good was the input model fit?\n\nLIMITATIONS:\nYes, the authors adequately provided the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript develops a detailed and accurate model of the fly visual system by extracting the complete connectome of the adult fly brain. It utilized conductance-based leaky integrate-and-fire neurons, with parameters based on Drosophila electrophysiology data, and employed Poisson spike trains to encode static gratings. The model successfully predicts the orientation map in the Drosophila visual system, suggesting that species that evolved independently can possess similar visual structures.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\nThis work uses the full adult fly brain connectome and conductance-based LIF neurons with parameters drawn from Drosophila electrophysiology data. The model predicts the orientation map in the Drosophila visual system. Preferred orientations derived from upstream dendritic geometry correlate closely with simulated tuning.\n\n**Weaknesses:**\n\n1. The manuscript uses a single, fixed stimuli drive and does not explore how input strength affects the orientation map formation. To assess the reliability of results, you can repeat the orientation map analysis with lower stimulus strength. For example, decrease the stimulus strength to 0.8, 0.6, and 0.4 times the original strength, and see coherent orientation map still emerges.\n2. I don't think this map can be called the orientation map, because the orientation map has a significant quantitative metric, pinwheel density = $\\pi$ (Kaschube et al., 2010, Science), and the map is perfectly periodic; the map also contains iso-orientation domains, pinwheel centers, saddle points, fractures, and linear zones. Moreover, nearest-neighboring pinwheel distance, pinwheel density, and hypercolumn size are available metrics to assess the quality of the orientation map (Ho et al., 2021, Current Biology).\n\n*Reference:*\n\nMatthias Kaschube et al. ,Universality in the Evolution of Orientation Columns in the Visual Cortex.Science330,1113-1116(2010).DOI:10.1126/science.1194869\n\nHo, C. L. A., Zimmermann, R., Weidinger, J. D. F., Prsa, M., Schottdorf, M., Merlin, S., ... & Huber, D. (2021). Orientation preference maps in Microcebus murinus reveal size-invariant design principles in primate visual cortex. Current Biology, 31(4), 733-741.\n\n\n3. The abstract claim that findings offer new insights into bio-inspired AI architectures. But the manuscript does not provide any experiments to support the claim.\n\nQUESTIONS:\n1. In line 51, the authors claim that \"maps often display salt-and-pepper rather than columnar architecture, as observed in both the retina and visual cortex [47].\" I agree with the latter part, but I don't agree with the former part. Vita et al. (2024; citation 47) demonstrate that mouse retinal orientation selectivity is organized into a continuous, location-dependent map, not a salt-and-pepper mix. Please refer to citation 47 carefully.\n2. Scientific acronyms should be spelled out once at first use; subsequent mentions should use only the acronym. For example, re-defining leaky integrate-and-fire “LIF” later (line 80) interrupts the reader’s flow. The second sentence in abstract is too broad, since many mammals (e.g., rodents) exhibit a salt-and-pepper layout rather than a map.\n3. I wonder whether lamina neurons L4 and L5 form horizontal connections with other lamina neurons. Specifically, do L4/L5 also synapse onto L1, L2 and L3 neurons? Or are their lateral connections only within L4 and L5 themselves? Please state it explicitly.\n4. The same question to the layer of the Distal medulla. Are there any horizontal connections within the layer?\n5. In Figure 2b, why are the peak firing rates of the neurons with OFF-bar stimuli higher than those with brightened stimuli? What is the mechanistic basis for the peak firing rates of the neurons with OFF-bar stimuli? Is it driven by synaptic connectivity or circuit dynamics?\n6. In Figure 2c, the data points are fitted by a Gaussian curve. But the fitting curve is missing from the right panel of Figure C.\n7. The manuscript does not specify how the input drive $v_i$ (membrane potential) is converted into the output firing rate $fr_i$. Could authors provide the explicit functional form or transfer function (f–I curve) used in the LIF model to relate $v_i$ to $fr_i$? \n8. Another question is about the inhibitory synaptic connections. Can you confirm whether any inhibitory (e.g., GABAergic) synapses were included in the LIF network? If so, please specify which neuron types provide inhibitory input, and detail the synaptic parameters used to model these inhibitory connections.\n9. In Figure 3b, the blue bars are labeled \"Total Neurons.\" Could you please explain what this term means? I suggest clarifying the definition of \"Total Neurons\" in the caption. \n10. In Figure 4, simulations use a static adult connectome to define all feedforward and lateral connections. Do you consider any developmental or activity-dependent changes in connectivity? Another question is whether the orientation map is convergent in Figure 4a, because it is not perfectly periodic.\n11. Another mistake is the orientation bar beneath Figures 4a and S3, the line icons meant to represent the orientation preference are mis-oriented. For example, the line icon with the label \"180\" is a horizontal line, but the manuscript says it is a vertical line. In Figures 4 panels a and b, S2, and S3, no plotting scale or colorbar is presented. \n12. The manuscript predicts that orientation maps in Drosophila are \"less discretely defined than in mammals, likely due to lower neuronal density\" (lines 278-281) To test this hypothesis, could you perform simulations with an effectively higher medulla neuron density (e.g. by subsampling at spatial resolution or interpolating additional cell positions) and assess whether map quality improves? In particular, please quantify map quality using metrics such as nearest‐neighbor pinwheel distance, pinwheel density, and hypercolumn size (you can refer to references in the Weaknesses Section).\n13. I am curious about whether the emergent pinwheel orientation maps in your model give rise to sparse population coding? (Reference: Vinje, W. E., & Gallant, J. L. (2000). Sparse coding and decorrelation in primary visual cortex during natural vision. Science, 287(5456), 1273–1276.) If it does, this work will potentially extend the population sparse coding from the primary visual cortex to the fly's visual system.\n14. What are the functional advantages of the orientation map of the fly's visual system? (Just curious, I would like to hear authors' opinions.)\n\nLIMITATIONS:\n1. Why do authors claim that \"may reflect an adaptation for encoding object boundaries or motion\" (line 283)? I agree that the orientation map can detect the boundary of the object, as it covers the whole visual field orientation. But I don't know why the authors think it is an adaptation for encoding object motion. Are there any experimental simulations or recommended references for this?\n2. In lines 303-304, \"optimise early visual processing, enhance perceptual adaptation, and provide a foundation for complex visual tasks\" is a bit vague. How to optimize processing, perpetual adaptation, and complex visual tasks? At the same time, the manuscript does not provide any simulations or experimental data to support the claim.\n3. The manuscript demonstrates that orientation maps can emerge in the Drosophila visual system, but the specific circuit mechanisms are not clear. Could authors clarify the mechanism for the emergence of the orientation map in the Drosophila visual system? Some ablation studies are needed at least.\n4. Other limitations, like a lack of experimental data to support the claim, a simplified LIF model omits nonlinear firing dynamics and neurotransmitter dynamics, are discussed in the manuscript.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work computationally demonstrates the existence of orientation maps in the medulla of the drosophila visual system by simulating a network of leaky integrate and fire neurons with the measured connectome of drosophila. They convincingly showed the existence of such maps (at least in the computational model), and moreover showed that these maps have a shared columnar organization as a function of depth.  They even found a nice surprising result that early photoreceptors could show orientation selectivity and explained this in terms of recurrent loops from higher neurons.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1) The work is extremely thorough and careful.\n2) It provides of a nice overview of orientation selectivity across multiple species suitable for a general reader.\n3) It is well grounded in the biology of the drosophila nervous system.\n4) It is an excellent example of going from a nonfunctional connectome to functional predictions.\n\nWeaknesses\n1) The robustness of the final results to parameter variations is left unexplored. For example, while many of the 10 parameters in appendix B.1 are set to fixed values found in the literature, it is unlikely that every neuron in the Drosophila brain has the same value for all parameters.  If there were an X% random change in all parameters (subject to sign constraints) how would the results degrade as a function of X?  If there were X% variations in each parameter from neuron/synapse to neuron/synapse, how would the results change?\n2) It was unclear from reading Appendix B how w_{j,i} was chosen.  Is it simply 1 if there is a connection in the connectome, and 0 otherwise?  If so that should be made clear, as that would then be a strength of the paper.  If not, it should be explained how w_{j,i} was chosen and why - if this choice was sophisticated then more information than the connectome was used to define the simulation.  Also in equation 3 of appendix B should it be w_{syn} * w_{j,i} as the text suggests?\n3) Finally, the existence of orientation selectivity/maps was already predicted qualitatively in prior cited work.  What does this quantitative measurement of the map in an insilico model add *conceptually* to our understanding above and beyond what has been said before?  A discussion of this point would be helpful.\n\nQUESTIONS:\nPlease see my questions in the weaknesses section above.  I am giving this paper the benefit of the doubt, assuming the answer to these questions are satisfactory (especially the first two).  If not, I may have to lower my score.\n\nA final question, which would be nice to discuss, are possible reasons for the diversity of the presence or absence of orientation maps across many species in the phylogenetic tree.  Is the presence or absence of smooth orientation maps, versus salt and pepper organization, a historical accident of evolution, or are there deep computational principles that dictate which will be found in which species?  How does the potential of smooth orientation maps in the fly impact the answer to this question?\n\nLIMITATIONS:\nYes. Subject to my questions in the weaknesses section above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a computaional model of the the brain of the fruit fly (Drosophila melanogaster). The authors simulate the responses of the neurons to different visual stimuli, and their analysis reveals the existence of orientation maps in the optic lobe, thus providing computational evidence for the previous hypothesis of such existence. This works shows that the orientation maps can emerge also in simpler non-mammal species visual systems.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- the paper has novel scientific contribution by providing evidence of existence of orientation maps in the fruit fly vision system\n- The authors provide detailed analysis of the results on neuron types level which can provide new insights to neuroscientists about the role of different cell types in visual perception.\n- They use statistical measures and compare computational predictions with anatomical structure.\n\nWeaknesses:\n- As a NeurIPS audience, I am more interested in details of the computational modelling of the brain in the main paper, how many neurons and synapses were modelled in total, and novelties in this regard.\n- The paper only tests orientation grating rather than natural scenes.\n- The paper claims that the results offer insights to design of efficient scalable AI models (L 305). One should be cautious with such claims without any concrete suggestions or methods.\n- Some related works are not discussed. See: \"A functionally ordered visual feature map in the Drosophila brain\", Klapoetke et.al. \n\nMinor: \n- Figure references are not linked to the figure. (Use \\ref)\n\nQUESTIONS:\n1. How did you chose 0.7 and 0.4 for the criteria of well-fit neurons? Can you provide a plot of R^2=0.7 (Similar to Figure 2-c)\n2. How many total synapses and neurons did you model?\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}