Connectome-Based Modelling Reveals Orientation Maps in the Drosophila Optic Lobe
Authors: Jia-Nuo Liew
Shenghan Lin
Bowen Chen
Wei Zhang
Xiaowei Zhu
Wei Zhang
Xiaolin Hu
Venue: NeurIPS 2025 poster ()
Keywords: Computational Neuroscience
Drosophila Visual System
Orientation Maps
Leaky Integrate-and-Fire Model
Connectome Simulation
Primary area: neuroscience_and_cognitive_science

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.

Connectome-Based Modelling Reveals Orientation Maps in the Drosophila Optic Lobe
Jia-Nuo Liew Shenghan Lin Bowen 2.46,77 Wei Zhangl
Xiaowei Zhu Wei Zhang2.4.6.711 Xiaolin Hu H228.
1 Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing 100084, China IDG//MGovernn Institute of Brain Research, Tsinghua University, Beijing 100084, China
ZZhili College, Tsinghua University, Beijing 100084, China
"School Life Sciences, Tsinghua University, Beijing 100084, China Ant Group, China
State Key Laboratory of Membrane Biology. Tsinghua University, Beijing 100084, China TTsinhha--eekinn Center for Life Sciences, Tsinghua University, Beijing 100084, China
8 Chinese Institute for Brain Research (CIBR), Beijing 100010, China
(1iujn24, linsh24, cbw21, zhang=23) tsinghua edu.
robert.2xw@ant www.antgroupppppp com com [wei_zhang, xlhu] @mail. tsinghua. edu.
Abstract
The ability to extract oriented edges from visual input 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 Dresophila 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, show that coherent orientation maps can emerge from purely connectome-constrae suggest that species of independent origin could evolve similar visual structures.
dynamics. These results
Introduction
The ability to extract oriented edges from visual input is a core computation across animal vision systems 23 Orientation selectivity is canonically attributed to the primary visual cortex (V1), where neurons respond selectively to specific edge orientations. Hubel and Wiesel 22 23 first demon- strated this modular structure in cats. The findings were later substantiated by electrophysiology. optical imaging, and detailed anatomical mapping K 41. These orientation--electiv responses are embedded within columnar and laminar architecture and are thought arise from combination of spatially organised feedforward inputs and recurrent cortical dynamics [2] 12
15 19 48).
Orientation selectivity has been directly observed in Drosophila melanogaster visual system. In particular, T4 and T5 neurons traditionally known for direction selectivity also exhibit robust orientation tuning, which sharpens motion detection [6] These findings provide physiological
"Equal contribution. CCoresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Anatomical organisation and connectivity in the fly visual system. (a) Schematic of the fly optic lobe showing the layered structure of the medulla, including the distal (Dm), serpentine (Sm), and proximal (Pm) sublayers. (b) Simplified connectivity diagram highlighting the visual pathway from photoreceptors (RI-6) through lamina neurons (LIILL3) to the orientation-sellctive neurons, the lobula complex.
Figure I:
evidence that orientation selectivity exists at the neuron level. Building on this, recent studies have hypothesised that neurons in early visual stages, particularly in the medulla, could support orientation selectivity, based solely on synaptic connectivity patterns from structural data AS]. Though the question of whether such orientation selectivity is organised into coherent maps across the visual system remains unknown. Given the compact, non-layered architecture of the Drosophila brain and the absence of large-scale recurrent loops seen in vertebrate cortex, it is unclear whether this system can support the emergence of global tuning structures such as orientation maps
To investigate this, we simulated the visual responses of carly-stage neurons (LI-L3) using bar-like stimuli and propagated their activity through a downstream population in the optic lobe modelled with leaky integrate-ane aad--free (LIF) dynamics, constrained by known synaptic connections [3]. We then analysed the resulting neural population activity, revealing spatially organised orientation tuning reminiscent of cortical orientation maps. Figure provides an overview of the anatomical structure of the Drosophila optic lobe, while Figure 1b illustrates the visual connectivity pathway examined in
this study.
To summarise, our main contributions are as follows:
We computationally demonstrated, for the first time, spatially coherent orientation maps in the medulla region of an invertebrate visual system.
We identified topological singularities and inter-layer columnar alignment in orientation preference within the distal medulla (Dm) and proximal medulla (Pm) regions.
Together, these findings suggest that canonical orientation maps can arise from shared computational principles across species, even the absence of cortical lamination.
Background and related works
Orientation maps in mammals. Orientation selectivity has been extensively studied in the visual cortices of many mammals. At the anatomical level, evidence from mammals indicates that orientation tuning can emerge neural substrates with vast differences [25] 38]. In rodents, maps often display palt-an--peppe rather than colummar architecture, observed in the visual cortex, while the retina displays a continuous topographic map orientation tuning 48 In contrast, cats and wallabies display structured pinwheel-like feature that has been documented in the primary visual cortex 24 22 [7)] despite differences cortical evolutionary divergence across mammalian species [41]. These findings suggest that orientation maps can arise across diverse mammalian species with varying

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cortical architectures, raising the possibility that such maps are not an exclusive feature of the neocortex but reflect convergent computational motifs.
Orientation tuning in non-mammals. Studies across non-mammalian species suggest that struc- tured visual coding arises under minimal anatomical constraints. In pigeons, sharply tuned orientation- selective neurons emerge through feedforward circuits alone, without laminated cortex [32). In turtles, despite the absence of fine retinotopy, population activity in the dorsal cortex accurately encodes spatial information [I7], indicating that global analysis of the visual scene can arise from distributed representations. Similarly, in zebrafish, orientation-selecet in the optic tectum, and some studies report their laminar segregation and retinotopic organisation [40] 443. However, while all three species exhibit orientation -selatioonseeeeiveee neurons, neither has demon- strated the presence of continuous, spatially organised orientation maps comparable to the ones found in the mammalian cortex.
neurons have been observed
Orientation coding in Drosophila. In Drosophila melanogaster, orientation tuning has been hypothesised based on anatomical structure alone. In particular, Seung [45] predicted that functionally distinct neuron types such as Dm3 and TmY may exhibit orientation selectivity, proposing that such responses could arise purely based on wiring alone. To date, the only direct physiological evidence of orientation selectivity comes from earlier studies on T4 and T5 neurons, which exhibit tuning to oriented edges in addition to their well-known direction selectivity [16]. Beyond these, no other neuron types have been experimentally or computationally confirmed to show orientation selectivity, Moreover, no prior study has computationally demonstrated the emergence of orientation maps in this non-cortical system. Our work fills in this gap by building on these findings to show that coherent, spatially organised orientation maps can emerge in such a system, thus suggesting that species of vastly different evolutionary origin may share common circuit-level principles for encoding visual features.
3 Methods
LIF model. We implemented a Ieaky integrate-and-fire (LIF) framework to simulate the spiking dynamics of neurons in the Drosophila visual system (see Appendix B.I) 46). Using the full adult fly brain (FAFB) connectome, we constructed a network which neurons are labelled and connected according to their anatomical synapses 8 Drosophila connectome, including 138,639 neurons and 1,508,983 synapses. The FAFB connectome
B 20 50 The model encompasses the complete adult
provides a complete and cell-resolved reconstruction of the Drosophila brain (50). and has become
foundational resource for structural annotation 44 functional inference [49 11, and whole-brain
spiking simulations validated against behaviour [46]. In our model, visual stimuli were simulated as Poisson spike trains injected into lamina inputs (L1-L3) in the right eye of the Drosophila, and the resulting activity was propagated through the whole brain connectome using the embedded LIF framework, allowing us to monitor orientation tuning in downstream visual neurons (Figure 2a
Stimuli. We focused the lamina neurons L1-L3, which are visual columnar neurons retino- topically organised neurons that are associated with an individual ommatidium in the compound eye, such that each visual column contains dedicated copy of the neuron (39]. Unlike other columnar neurons, LI-L3 receive direct input from photoreceptors RI-R6, which capture brightness change across the visual field. The ommatidia themselves are arranged in hexagonal lattice, forming precise sampling grid over visual space. For this study, we applied input directly to L.-L33 rather than R1-R6, as the connectomic dataset provides visual columnar mappings for LI-L3 but not for 35
36, 44 (50 (3). Naively, one can let an LI-L3 neuron receive fixed strength of Poisson input regardless of the location of the OFF-bar on the receptive field (RF) of the corresponding ommatidium. However, this ignored spatial gradients across the RF To better reflect spatial structure, the input stimuli were modelled by computing the distance d from each ommatidium to the bar and making the input strength depend on d based on the calcium imaging data (29] (Appendix B.2 Figure 2b illustrates how the distance of the Oar modulates input strength. In the top row, each panel shows single ommatidium, colour-coded by its corresponding 1.1-L.3 firing rate as a function of d from the OFF-bar (black line). Firing rates increased as the bar approached the centre of the ommatidia, peaking when aligned and tapering off' with distance. The
photoreceptors [8] [3] 14 (20 33
3

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Figure 2: Overview of the simulation framework and analysis methods. (a) Schematic of the simulation pipeline. Poisson spike trains were used simulate grating stimuli input LI-L3 neurons. Each neuron's ' orientation tuning is quantified by its firing rate across stimulus angles. (b) Structured stimulus input L1-L3 neurons across different OFF-bar positions (black line). Top: Each panel shows hexagon representing ommatidium. Bottom: 2D heatmap of firing activity across the simplified hexagonal lattice of ommatidia. Each hexagon represents one ommatidium, colour-coded by its corresponding LI-L3 firing rate. d represents the distance to the centre of the ommatidia. Please note that, even when the bar stimulus lies outside the geometric boundary of an ommatidium, measurable response is still observed due to the broader receptive field 5 8%) relative to the ommatidial spacing (4.6)), resulting spatial overlap of sensitivity across neighbouring uni (c) Gaussian turning curves of two neurons: the left panel indicates well-fit neuron (orientation- selective), and right panel indicates neuron classified as not orientation-selective (d) Structural prediction of preferred orientation angles based upstream columnar connectivity. The red dashed lines indicate the best-fit ellipse drawn through these spatial locations.
bottom row provides simplified illustration of the 2D activation pattern across the hexagonal lattice for several OFF-bar positions.
Preferred orientation. To quantify the orientation selectivity the monitored neurons, we fit Gaussian function to their firing responses across different stimulus orientations. Unlike standard Gaussian, which assumes a linear domain, our model accounts for the 180" periodicity of angle space by computing the minimal circular distance between the stimulus orientation and the neuron's 's preferred orientation (Figure 2c] see Appendix B.3). A was considered "good" it satisfied two criteria: coefficient of determination R² 2 0.7 and normalised residual sum of squares RSSmorm < 0.4 indicating both high explanatory power and low relative error (Figure 2c). Representative examples of Gaussian fits that considered good, poor and near threshold illustrated in Figure S4a The distribution of R2 across neurons and the relationship between Ii2 and RSSnarm illustrated in Figure S5bb [S4c Unless otherwise noted, the preferred orientation of neuron in this paper refers to its orientation preference as determined by this Gaussian fit.
Structural prediction. In this paper, structured prediction refers to the predicted preferred orienta- tion angle a neuron by its direct upstream neurons, similar receptive field approach used by identify each neuron's upstream partners that are also columnar neurons
Seung 45). Specifically, using known synaptic connectivity. We mapped these upstream neurons to their corresponding columns in the compound eye, and best-fit fit ellipse drawn around their spatial locations (Figure 2d).

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Figure 3: Orientation tuning and structural prediction accuracy. (a) Heatmaps showing the
normalised firing rates of various neurons across different angular orientations. (b) Proportion of neurons with well-fit orientation tuning across types. Green bars indicate the subset well-fitted by circular Gaussian model (see methods). Blue bars indicate the total number of neurons in the right optic lobe of the respective type. (c) Distribution absolute differences between each neuron's preferred orientations predicted by LIF simulation and by dendritic structure.
The orientation of the ellipse's 's major axis, measured relative to the y-axis, is taken as the predicted angle for the downstream neuron.
4
Results
4.1
Orientation selectivity
We first evaluated whether neurons in the Drosophila optic lobe exhibit robust orientation selectivity. Using our LIF-based simulation framework, we presented bar-like stimuli across orientations and recorded their firing rates. We then fitted each neuron's 's response profile using a circular Gaussian model (see Methods). For neurons that were well-fit, we normalised firing rates to their peak responses and visualised the resulting heatmap of preferred orientations. Neurons were initially sorted via hierarchical clustering, revealing structured patterns in tuning preferences. Upon observing a diagonal- -ikee structure suggestive of continuous orientation gradient we further sorted neurons by the ascending position of their peak response, which highlighted a clearer gradient in orientation tuning (Figure 3ap. Our findings demonstrated that Drosophila neurons exhibit robust orientation selectivity, with evidence suggesting that orientation maps in this species may exist. The percentage of well-fit neurons relative to the total neurons in the type for a subset of neurons (Dm3v, Dm3p, Dm3q. TmY4, Tm Y9q. TmY9q aligns well with Seung's prediction (45), indicating that orientation selectivity arises directly from the spatial arrangement of dendritic inputs (left two panels of Figure 3a
Moreover, we found that several other neuron types (e.g., Dm15, Tm33, TmY10) also exhibited tuning properties (Figure Ba). To quantify this selectivity, we classified neuron type as orientation selective if over 40% of its neurons were well (Figure 3b) see Methods). These results revealed that most ceeentatioo-sellcctii key site for early orientation processing in the Drosophila visual system. To further investigate the tuning mechanism and verify hypothesis that orientation tuning could be directly influenced
neurons were located within the medulla layer, highlighting it as
5

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by its dendritic inputs, we quantified the preferred orientation of each neuron by computing the structural prediction of the neuron (Figure 2aj see Methods). We then compared it to the neuron's preferred orientation using absolute difference. Across the population analysed, the mean absolute difference sits at 13.7% (Figure (3). indicating a robust correspondence between dendritic geometry and orientation preference. Notably, T4 and T5 neurons, despite being known for their direction and orientation selectivity, did not meet our selection criteria in this analysis. Our results showed that most T4 and T5 neurons had firing rates close to zero. A few neurons that had higher firing rates exhibited orientation selectivity (Figure (5). This was likely due to the stimulus design: the static input may have been insufficient to strongly activate these neurons.
Intriguingly, while analysing the orientation tuning patterns across neuron populations, we incidentally observed that photoreceptor R7 and R8, classically known for detecting colours [36]. also exhibit
orientation selectivity (Figure (b). This unexpected finding raises the question of how such tuning arises in primary sensory neurons. We investigated the connectivity of the neurons and found that Dm9, R7 and R8 formed closed recurrent loop that results in R7 and R8 adopting the same orientation preferences Dm9 (Figure Ib and right two panels of Figure Ba). which itself receives input from L3. This closed recurrent loop between Dm9, R7 and R8, leading to synchronised orientation preferences, could provide new insights into how feedback within small circuits may amplify or stabilise neural responses. While this was not the primary hypothesis, it raises interesting questions about the role of recurrent circuits in orientation selectivity. Further exploration of this feedback mechanism in the future could contribute to our understanding of how local orientation selectivity is maintained or enhanced within neural networks.
4.2 Orientation maps
To evaluate whether orientation selectivity is spatially organised in the fly visual system, we focused on the medulla, a key visual processing region that receives direct input from LI-L3 neurons and contains the highest density of neurons with strong orientation selectivity (Figure 3b). Across the medulla sublayers which are anatomically divided into distal (Dm), serpentine (Sm) and proximal (Pm) layers, shown in Figure Ial we identified 1710 neurons in the Dm layer, of which 1245 belonged to the Dm3v, Dm3q and Dm3p subtypes. Additionally, we identified 143 well-fit neurons in the Pm layer and 63 in the Sm layer, based on the Gaussian tuning criteria (see Methods). It was found that neurons in all three sublayers exhibited clustered patterns- neurons with similar preferred orientations tended to cluster together (Figure (S) We then focused on the Dm and Pm layers for detailed spatial analysis, as these layers contained a sufficient number of neurons spanning the full spatial extent of the medulla (Tigure(S6))
We first visualised the spatial organisation of preferred orientation by flattening the 3D morphology of the neurons in the Dm and Pm layers onto layer-speeific 2D coordinate systems. To achieve this, we computed a best-fit plane through the full set of 3D skeleton coordinates from each layer, providing a reference frame for spatial alignment and visualisation. This approach preserved the columnar layout while minimising distortions introduced by the Drosophila's 's optic lobe curvature. To enable cross-layer comparisons, a scaling transformation was applied to normalise for differences in physical size and curvature between the Dm and Pm regions. This projection preserves relative topographic relationships while enabling 2D spatial smoothing and angular comparisons. We colour-coded the neurons into their preferred orientation across Dm and Pm layers using unsmoothed maps to examine the spatial distribution of preferred orientations (Figure (S6). These maps revealed a clear spatial clustering of similar orientation preferences, in contrast to a salt-and-pepper pattern in the rodent V1 area.
To better visualise the global structure, we applied local spatial smoothing by computing the circular mean of preferred orientations within circular neighbourhood of fixed radius around each neuron. Specifically, for each neuron in the 2D projected plane, we identified all neurons located within a disk of radius x 5 x 10 nm and computed the circular mean of their orientation preferences. This smoothing preserved mesoscale spatial patterns while reducing local variability. The resulting heatmap produced orientation maps shown in Figure 4a| These smooth maps reveal pinwheel- singularities points around which orientation preference rotates continuously (the circled points in Figure Hap- indicating degree of spatial coherence previously unreported in invertebrate visual systems.

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Figure 4: Spatial and orientation map in the optic lobe. (a) The smoothed heatmap of the optic lobe coloured by its preferred orientations at each layer of the optic lobe. The circles highlight the observed pinwheel structures. denotes the position angle where dorsal-ventral represents 0 and denotes the preferred orientation of the neurons (see Methods). (b) Vector field plots of the preferred orientation in the Dm (top) and Pm (bottom) layers. Each line represents the preferred orientation of neuron, and the background heatmap encodes the absolute angular difference between the neuron' 's and 3. Green regions indicate strong tangential alignment (A Rs 90%), red regions indicate radial alignment (A s 0%). (c) Scatter plot (position angle, preferred orientations) pairs are displayed with coordinate axes shifted for visualisation (y-axis: -90 to 270", x-axis: 0" to 360%). (d) Relationship
of la 3 and D: dorsal area; V: ventral area. (c) Relationship between la and radial distance from the centre.
Initial observations of Figure 4a revealed that both layers exhibited a roughly centrosymmetric distribution of preferred orientations. To characterise this structure more precisely, defined polar coordinate system for each layer, with the origin set at the centroid of the neuron population, computed by the mean of the spatial coordinates, and using the V-D (bottom-top) direction as the axis. This allowed us to compute spatial position angle for each neuron and directly compare Building on this
its preferred orientation B, derived from the smoothed map in Figure smoothed representation, we treated each neuron's 's orientation as representative of its surrounding neighbourhood, based on the same local averaging kernel described earlier. To quantify alignment, we computed the angular difference A = |a 31 for each neuron and projected it onto 2D heatmap (Figure (b)), where A 90" indicates tangential alignment and A 0% indicates radial alignment. This visualisation revealed a clear trend: neurons in peripheral regions tended to prefer tangential orientations (B &e a + 90%). These findings suggest that orientation tuning in the medulla is not randomly distributed, but spatially organised relative each neuron's 's anatomical position.
We then further examined the relationship between and I for all neurons in Dm and Pm layers. Here, each neuron's 's position angle was computed as the circular mean of the angular positions of all its voxels in the 2D projection. The resulting seatter plot showed clear diagonal band, consistent with a systematic offset where B & + 90", supporting the presence of tangential alignment 4c). To quantify this relationship, we computed A and examined how it varied across spatial dimensions. Across angular bins, the A remains relatively stable with minor regional differences

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Figure Orientation column structure in the optic lobe. (a) Three examples orientation columns in the Dm layer, colour-coded by preferred orientation. (b) Three examples of orientation columns in the Pm layer, colour-coded by preferred orientation. (c) Quantification of tuning consistency within columns. Top: Dm layer. Bottom: Pm layer. *** indicates significance assessed via Mann-Whitm test comparing standard deviations to baseline (p < 10 10-20).
(Figure 4d). In contrast, alignment improved monotonically with radial distance from the centre (Figure Me indicating stronger alignment the periphery. Although perfect tangential alignment would yield A = -90", the observed means approaching 90º suggest systematic but approximate offset. We speculate that radial and tangential recognition neurons organise an overall pattern in the medulla corresponding to the horizontal direction. This pattern is especially evident in the Dm layer, suggesting a non-uniform organisation of orientation preferences, which may play key role in the previously reported recognition of horizons and horizontal objects by the biological flight control system 477.
This finding suggests that orientation tuning in Drosophila's 's optic lobe is not randomly distributed but exhibits structured, position-deeendent organisation. To access the contribution of upstream visual pathways to orientation selectivity, we performed targeted ablation analysis (Appendix C). Silencing Mi neurons abolished tuning across both Dm and Pm layers, while silencing Tm neurons produced selective loss in Pm but minimal effect in Dm, suggesting that Mi neurons provide essential input for orientation tuning throughout the optic lobe. Consistent with this observation, an analysis of synaptic connectivity by orientation preference (Appendix D revealed that excitatory neurons with similar tuning are more likely to form recurrent connections, supporting topographic organisation and collinear facilitation as proposed by Seung [45]. In contrast, inhibitory connections displayed distinct off off-diagonal structure, suggesting selective cross-orientation motifs that may sharpen tuning and maintain balance within the network. Peripheral neurons preferentially align their orientation selectivity tangentially relative to the centre of the optic lobe, reminiscent contour- aligned representations seen in higher animals.
4.3
Orientation columns
We next asked whether such tuning is preserved across the depth of the medulla. We examined the existence of column structures clusters of neurons aligned vertically across layers that share similar orientation preferences. Putative orientation columns were defined by anchoring each analysis region around Tm3 neuron, chosen due their well-defined columnar morphology. For each anchor neuron, we extracted the surrounding neurons within cylindrical region of 1.5 x 10a nm radius for both the Dm and Pm layers according to their skeleton coordinates. Each such grouping was considered single column. A total of 858 columns were extracted, each containing average

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approximately 158.29 neurons in the Dm layers, of which 124.14 belong to Dm3 and Dm15 neuron types and 29.7 neurons in the Pm layers.
We visualised the neuron morphology by projecting a rectangular slice passing through the centre of the cylinder onto 2D plane. This plane was defined by the two orthogonal axes perpendicular to the principal axis - the longest direction along which the neuron's morphology extends of the Tm3 skeleton, ensuring that the projection aligned with the local column geometry. Specifically, neuron positions were transformed into this local coordinate frame by projecting each skeleton point onto the two orthogonal basis vectors to the Tm3 axis. This allowed us flatten the 3D column into a 2D view while preserving vertical alignment across layers. We randomly selected three distinct columns each from Dm (Figure Ba) and Pm (Figure (5b) for visualisation. The Dm layer did exhibit orientation column organisations, with vertically clustered neurons sharing similar orientation preferences. However, Dm3 (Dm3v, Dm3p, Dm3q) and Dm15 neurons seemed to form distinct horizontal layer that bisects each column into upper and lower segments, creating clear visual stratification within individual columns (Figure This pattern was observed consistently across columns, indicating widespread structural feature in the medulla. Notably, these neuron types exhibited only a single orientation preference (Dm3v: Rs 0% Dm3p: R 60%; Dm3q: R 120%; Dm15: x 100%), in contrast to other types that exhibited broader tuning curves or gradual changes in preferred orientation across space (Figure 2a). On the other hand, the Pm layer also exhibited orientation column structure (Figure 5b), though there a few columns that seem less pronounced in the structure (left two panels of Figure 5b).
To quantify this alignment, we measured the circular standard deviation (see Appendix B.4) of pre- ferred orientations within each column in the Dm layer. The circular standard deviation was computed over the preferred orientations of all voxels within the column region, providing measure of how tightly aligned the local tuning preferences are. Lower values indicated stronger orientation coherence within the column. To further ensure that the observed orientation columns were not artefact of the simulation framework or analysis pipeline, we generated a null baseline by randomising the preferred orientations across neurons while preserving their spatial locations and column definitions. The resulting distribution revealed that most Dm columns, excluding Dm3 and Dm15, exhibited sharp tuning, with peak at low deviation values and minority displaying broader orientation spread (top panel of Figure We repeated the same analysis for columns in the Pm layer. In contrast to Dm, the Pm layer showed weaker columnar correspondence, with a distribution of standard deviations centred closer to the shuffled baseline (bottom panel of Figure (c)). Compared to the Pm layer, the Dm layer exhibited a more pronounced separation between the observed and baseline distributions, indicating stronger tuning coherence.
Together, these results demonstrate that Drosophila's 's optic lobe contains not only intra-layer orien- tation maps, but also orientation columns across different layers of Dm, reminiscent of columnar architectures in vertebrate visual systems.
5 Conclusion and Discussion
In this study, we investigated the orientation preferences in the Drosophila visual system and explored the spatial organisation of these preferences, revealing structured pattern. While these maps are less discretely defined than in mammals, likely due to the lower neuronal density, the presence of spatially organised orientation selectivity and columnar structure suggests that Drosophila possesses a rudimentary form of orientation map. Biologically, the organisation shows a population-level bias toward tangential tuning, where neurons prefer orientations aligned with the local contour of the visual field and may reflect an adaptation for encoding object boundaries or motion, enhancing contrast sensitivity across the curved surface of the compound eye (6). The results align with studies in other mon-mammalian species, such birds, turtles and zebrafishes, where sharply tuned prientation-selectee neurons are found in lom-namdd visual pathways as mentioned earlier [32 [7 40 43). However, as of now, Drosophila is currently the only invertebrate where spatially structured orientation preferences, resembling primitive orientation map, have been computationally demonstrated. As such, we hypothesise that structured visual maps may reflect convergent evolution, arising from shared computational demands rather than shared anatomy. The Drosophila ommatidium > Dm - lobula circuit may serve a functionally analogous role to the
mammalian retina - LGN + V1 pathway [ 19 27]. suggesting that efficient visual processing
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follows common architectural motifss across species. Mechanistically, dendritic integration, closed- loop motifs, and lateral inhibition likely contribute orientation tuning in Drosophila, as proposed Consistent with this interpretation, analysis of synaptic connectivity by orientation preference (Appendix D revealed that neurons with similar tuning are more likely to form recurrent connections. The study by Klapoetke et al. (30) reveals functionally clustered representations of complex visual features in the lobular columnar neurons projecting into the central brain. In contrast, out work focuses on orientation selectivity in the earlier stages of the optic lobe, based on connectome-driven circuit structure. Together, these complementary findings highlight how structured visual maps may extend from early visual encoding to higher-order feature integration within the Drosophila brain.
in prior works 2
25 45
The orientation maps at the early stages of visual processing provide critical advantages for efficient visual detection for the flies. The neurons that selectively respond to similar orientations are spatially adjacent to each other, enabling them to fire maximally when line or edge appears at their preferred orientation, which in mammals is achieved by long-distance connections among columns with similar orientation preference. The columns are arranged in a primitive pinwheel-like map, enabling more stable and precise edge perception. This organisation supports higher-level vision by feeding processed orientation data to the central brain for shape recognition and motion detection. Drosophila serves as a powerful minimal model for exploring core principles of vision, with potential applications in bio-inspired machine vision and low- low-power autonomous systems.
This study relies on biologically grounded simulations and lacks in vivo validation, warranting experimental follow-up to confirm our predictions. Similar to prior work based on anatom- ical reconstructions [45], our approach emphasised predictive modelling to inform future experiments. The use of simplified LIF model omits nonlinear firing dynamics and neurotransmitter effects, which may affect network behaviour. However, similar simplified models have been effectively used in large-scale cortical simulations to gain insights into emergent network dynamics 46 18]
Limitations.
Acknowledgments and Disclosure of Funding
This work was supported by the National Key Research and Development Program of China under Grant 2021ZD0200301, the National Natural Science Foundation of China under Grant U2341228, the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China under Grant JYB2025XDXM504, and Ant Group under Grant 20252000099.
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Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
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Question: Are the creators or original owners of assets (e.g.. code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
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The paper properly cites the FAFB connectome and other publicly available Drosophila datasets, referencing the original sources.
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Supplementary Material
A Code and data availability
The full code and data can be found at htttt:://githh// http://tttt...ooo//////////to/ot orr maps The model ran an Intel(R) CPU at 2.90GHz machine using 20 threads in parallel. Each thread required approximately 72 hours to complete a single round simulation of different angles in the range 0" 180 with interval of 0.4"
B Experimental and analysis definition
B.1 LIF model design
We implemented conductance- based leaky integrate-and- and-fire (LIF) model and integrated the FAFB dataset into this LIF model to run our simulations 8 The membrane potential V/ neuron evolves as:
9
21
26
28
46
4
20
35
36
44
50).
de:
9t (0%
Vresting)
dt
Tibe
dg:
gi
gi gi (Wis Wayn) upon spike from neuron j.
spike emitted when v 2 Viheeshold, after which the membrane potential is reset to Vrese for refractory period redructory. Synaptic transmission is delayed fixed latency Tay. The model used the following parameter values obtained from Drosophila modelling or electrophysiology efforts
9 26
28
42 (6);:
V Yesting
52 mV: resting potential 28
46
Vreset
52 reset potential
46
Vihreshold = 45 mV: spiking threshold
28
46
Rmbr 10 k2 cm? membrane resistance 28
46
Cmbrr μF cm -2; membrane capacitance 28
46
Tibr = Rmbr Cmbe: membrane time constant
46
Trefmetory- 2.2 refractory period 28
31
46
T = 5 ms: synaptic decay time constant 26
46
Tayy = 1.8 ms: synaptic transmission delay 46
Weyn 1.5 mV: synaptic weight; free parameter
9:: the synaptic conductance resulting from the aggregate firing of neurons presynaptic to neuron i.
Wayn is free parameter representing the strength of excitatory and inhibitory postsynaptic potentials. The value was chosen to ensure sufficient firing activity across the medulla for analysis, given the low baseline firing rates of visual neurons. The number of synapses of an upstream neuron j downstream neuron is represented by the connectivity Wjai such that if neuron j fires, the membrane potential of neuron changes in proportion to its connectivity Wy,i Wayn.
To assess sensitivity to parameter perturbations, we performed variation analysis where all parame- ters (except Vrest and Vihreshold) were randomly perturbed up to a fixed maximum percentage (Figure Sla 10% variation, we observed minimal changes in preferred orientation across neurons (Figure STb At 50% orientation selectivity was noticeably degraded (Figure STc however, surprisingly. the ODEs (Equations]] [ and remained stable and did not diverge. This suggests that the system retains degree of functional robustness even under large perturbations.
22

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Preference angle difference
10*
-10"
A1 by 20%
A2 by 20%
A3 by 20%
Vary 10% 60% input intense Vary 50%
Parameter adjusted
(a)
ff
(b)
Figure S1: LIF model paramcter validation. (a) Shows the difference in neurons' preferred orientation angles when stimulus parameters (AI, A2, A3) are increased by 20% respectively. "Vary 20%" indicates simulations where all LIF parameters (except Vreat and Vihreshota) were randomly perturbed by 100. "60% imput intense" refers to simulations where all inputs were reduced to 60%. "Vary 50%" is similar to "Vary 10%" but instead is randomly perturbed by =50%. (b) Orientation maps under 10% LIF parameters variation. (c) Orientation maps under 50% LIF parameters variation.
B.2 Stimulus designs and validation
To simulate early visual input, we designed synthetic stimulus that mimics the spatial layout and angular sampling properties of Drosophila photoreceptors. This allowed us approximate the input that LI-L3 would receive in vivo. Our model design was guided by calcium imaging data reported in Ketkar al. [29]. which showed that L3 neurons exhibit the strongest response variation across changes in light intensity. followed by LI, with L2 showing the weakest modulation. These responses increased nonlinearly with light intensity, starting slowly and then rising more steeply. To capture this, we defined each neuron's 's firing rate as function of spatial distance from bar stimulus, which we refer to as the OFF-bar.
We assumed that spatial distance from the stimulus approximates variations in local light intensity the visual field. The firing rate of neuron group LI-L3 was then defined as:
A Be-
/1 d2 max(fr)
if [d] < 1,
FiringRate, r.n. (d)
(4)
otherwise,
where d denotes the distance from the centre of the ommatidium to the OFF-bar in the visual field, measured in units where corresponds the distance between the centres of two neighbouring ommatidia. max(fr) represents the maximum firing rate, set to 200Hz. The parameters A, and B,, layer-specific gain and baseline levels, were estimated by fitting model responses experimental data 29
L1: A 7, Bi 20 Hz
L2: A2 5, B2 20 Hz
L3: Ag 10, B3 20 Hz.
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Inpux strength (MS)
Rospoense rate
Distance
(a)
(b)
-
Dm
Pm
Dm
Pm
(c)
(d)
Dm
Pm
(c)
Tml
Respoerise fiitng (HZ)
Tm2
Mi4
Tm4
Tm3
(f)
(g)
Figure S2: Activation of neuron types bar-like stimuli. (a) Modelled input strength profiles for lamina neurons LI-L3 as function of distance from the OFF-bar stimulus. (b) Firing rate traces of representative neuron from each of the LI-L3 types against their distance from the Oar- The red line indicates where the stimulus OFF-bar is located. (c) Orientation maps of where A1 parameters are varied by 10%. (d) Orientation maps where A2 parameters are varied by 10%. (e) Orientation maps of where A3 parameters are varied by 10%. (f) Firing responses of representative downstream OFF-response neuron from each of the Tm1, Tm2 and Tm4 types against their distance from the OFF-bar. In both (f) and (g), neurons were randomly sampled to verify that OFF-bar stimuli reliably activated these neuron types as expected.
representative downstream ON-response neuron from each of the Mil, Mi4 and Tm3 types against their distance from the Oar- OFF-bar stimuli reliably suppress firing rates in these neurons within OFF-bar regions.
(g) Firing responses
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The configuration (Ag > A1 > A2) captured the relative sensitivity of L1-L3, as shown in Figure S2a, The baseline parameter B, accounted for residual activity even when the calcium signal is low or flat, as AF/F FF 0 does not necessarily imply zero firing due to background fluorescence and imaging noise. We therefore set B₂ 20Hz to reflect plausible baseline activity. To assess the robustness of our results to the gain parameters Ag, we conducted a sensitivity analysis by decreasing each of A1, A2 and A3 by 20%, one at a time. The changes in well-fit downstream neurons' preferred orientation (measured by preference angle difference) remained small across all conditions (Figure Sla and the qualitative tuning properties were preserved. Similarly, we plotted the orientation maps of where A1, A2 and A3 parameters were varied. We observed minimal changes to the orientation selectivity (Figure S2c S2d S2e).
To ensure that the simulated stimulus was able to elicit stimulus-consisten pattern among lamina, which is convinced to direct downstream of the photoreceptors, we examined the spatial and temporal activation patterns of lamina cells L.-L.3 and their downstream neurons. Since our stimuli (OFF-bars) presented straight bar pattern, we examined the firing rate of lamina cells when the OFF-bars were at different distances from them. We investigated the firing rate of lamina cells in response to the OFF-bars positioned at varying distances from them. Our findings revealed a clear trend: as the distance between the OFF-bars and the cell increased, the firing rate decreased correspondingly. Notably. the lamina cells (L1-L3) exhibited a peak firing rate when the OFF- bars were precisely aligned with the cell's location (Figure S2bb). These results confirmed that artificial cimuluusdriveed lamina neuron activity in our digital Drosophila brain was consistent with the L1-L3 response properties known from biological experiments 29]
We then sampled set of downstream neurons specifically Tm1, Tm2, Tm4, Mil, Mi4 and Tm3, which are known from anatomical studies to be postsynaptic targets of L1-L3 neurons [3.. These neuron types were not shown in Figure [b] to maintain visual simplicity, but their connectivity is well established. We randomly selected single neuron from each of the Tml, Tm2 and Tm4 subtypes as they are known OFF-cells (36 and evaluated if they exhibited firing patterns that reassemble the expected response profile to OFF-bar stimuli that is, peaking when the bar aligns with their receptive field and decreasing with distance. As shown in Figure S2f, these OFF-responsive downstream neurons exhibited patterns expected. In contrast, of the Mil, Mi4 and Tm3 subtypes, known ON-cells [36.. and evaluated whether they showed the opposite trend stronger inhibition, when the OFF-bar nearby (Figure (22g). These neurons' spatial activation pattern mirrors the OFF-bar's location on the compound eye, consistent with known visual topology (3)..
also sampled single neuron from each
B.3
Circular Gaussian function
We calculated the preferred orientations of each neuron using circular Gaussian function defined as:
FiringRate, (0) = C. exp( d(0,
B + D.
(5)
where:
FiringRate, (0): predicted firing rate of neuron n at the orientation 0
0: input orientation (in degrees, 0" < 180")
A: preferred orientation (in degrees, fit parameters)
B: width parameters (sharpness of tuning)
C: amplitude the tuning curve
D: baseline firing rate
d(0, A) min( (10- AI, 180 - 10 Al): smallest circular distance between input orientation and preferred orientation.
B.4 Circular standard deviation
We quantified the sharpness of orientation tuning within each column using the circular standard deviation (see Figure 5ch in the scipy Python library, defined
1n
(6)
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Table S1: Ablation study results.
Z
Tm silenced
Mi silenced
Dm
90.7%
2.4%
Pm
10.4%
3.9%
where:
circular standard deviation (in radians)
R: mean resultant length, computed as:
R
cos )2
sin a;
0gi preferred orientation of neuron i (in radians)
number of neurons in the column
This metric captured how tightly clustered the orientations are around the mean. A value of 8 = indicates perfect alignment, with larger values reflecting broader tuning distributions.
C Targeted ablation analysis
To evaluate the contribution of specific upstream visual pathways to orientation selectivity, we performed targeted silencing of key input neuron populations. Using the equation: 2% X/Y, where X represents the number of good fits in ablation, Y represents the number of good fits in the original result, and Z is the percentage neurons remaining that still exhibit orientation selectivity. Silencing Tm neurons resulted in an 89.6% reduction in well-fit orientation-selecte Pm layer but only 9.3% reduction in Dm. In contrast, silencing Mi neurons eliminated tuning across both layers, leaving fewer than 5% of well-fit neurons in either Dm or Pm (Table(ST). These findings suggest that Mi neurons represent essential upstream sources of orientation selectivity throughout the optic lobe.
neurons in the
D Orientation-based synaptic connectivity structure
To examine whether recurrent connections in the connectome reflect functional similarity, we quan- tified synaptic connectivity represented as a point (0p, 0y.) where denotes the preferred orientation (in degrees) the pre- and postsynaptic neurons, respectively. In the excitatory subnetwork, connections cluster along the diagonal (0p, se 9,,), indicating that neurons with similar orientation preferences are more likely to be recurrently connected (Figure S3ab. This pattern consistent with topographic organisation and colinear facilitation as proposed by Seung (45), supporting the notion of like-to-like connectivity in local circuits. In contrast, inhibitory connections exhibit off-diagonal structure, suggesting cross- orientation interactions and selective suppression mechanisms (Figure S3b). These results suggest that recurrent circuits the Drosophila optic lobe preferentially link neurons with similar selectivity, providing network- level evidence for structured functional organisation within the connectome.
function of orientation preference. Each synapse (p; 9:) was
E Extended figures
Figure S4 shows the Gaussian model fitting used quantify orientation selectivity. Figure S5 shows the orientation tuning profile of T4 and T5 subtypes. Figure[Só) presents additional visualisation of orientation maps across different neuron types and layers within the optic lobe.
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Connectivity by Preferred Direction
Syraptic Connectivity by Preferred Direction
20" 0-
12000
Prusyraple Neuron inefered Angla idaquass)
Angle (degree))
5000
40"
10000
60"
-600
Synspti We gt
80"
8000
1000
1008
6000
170"
140"
180
1000
100"
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40"
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100"
120"
140"
160*
100"
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801
100"
120
1n10 160"
180"
Rostsynaptic Nouron Preferred Angle (oegress)
Postsynaptic Nouron Preterred Angle (oegress)
(a)
(b)
Figure S3: Orienfi synaptic connectivity, (a) Excitatory network showing strong diagonal structure, indicating neurons with similar orientation preferences preferentially connect. (b) Inhibitory network showing off-diagonal structure, consistent with cross-orientation suppression motifs.
20
R-= 0.95
20
R-= 0.74
20
R 0,22
RSS = 0
RSS 0
RSS
0
Firing rate
Firing rate
Firing rate
10
10
data
data
fit
fit
data
34
82
130
178
150"
18"
66
114"
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157
25 Angle
73
Angle
Angle
(a)
Distribution squared
Point Cloud: RSS (norm) vs R squared
squared
6000
2000
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
R squared
RSS (normalized)
(b)
Figure S4: Model Gaussian fit quality. (a) Representative neurons where their Gaussian fit is considered good (left), borderline (middle) and poor (right). (b) Distribution of R² values, where the red line indicates the threshold. (c) Distribution of R² and normalised R.S. of all neurons in the right optic lobe, including neurons in Figure S4a
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T4a, T4b, T4c, T4d
Tsa, T5b, T5c, T5d
0
90
180
0
90
180
(a)
(b)
Figure S5: Orientation tuning profiles some T4 and T5 neurons. (a) Heatmap showing orientation tuinng responses of T4 neurons that are well-fitted. (b) Heatmap showing the orientation tuning responses of T5 that are well-fitted.
Figure S6: Orientation preference structure across different neuron types. Preferred orientation
maps across optic lobe layers before smoothing, colour-coded by angle.
28


Reviews:
[{"review_id": "xRhHByUcID", "reviewer": "Reviewer_fCei", "summary": "The 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.", "questions": "1. 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?", "limitations": "yes.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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)", "was_revised": true, "final_justification": "After reading the rebuttal I will remain with my original score and recommend acceptance of this paper.", "rebuttal": "Thank you for the constructive feedback. Below, we address the main concerns and clarify key points raised:\n\n1.\tWe appreciate your interest in the modelling details. Our model includes a total of 138,639 neurons and 1,508,983 synapses (as in the connectome dataset), covering the full Drosophila brain connectome. We will revise the manuscript to state these details explicitly.\n2.\tThank you for the suggestion of using Natural Stimuli rather than artificial stimuli. We agree that incorporating naturalistic stimuli may reveal other encoding mechanisms and is a valuable direction for future work. However, our current use of oriented bar stimuli was a deliberate design choice to isolate orientation tuning mechanisms.\n3.\tIn response to the AI claim, thank you for the helpful suggestion. Our intent was to highlight the broader insight that minimal biological circuits can support structured visual representations. We will revise the relevant statement to reflect this more cautiously, emphasising biological significance over direct AI application. \n4.\tRegarding the missing relevant works, thank you for pointing this out. The study by Klapoetke et al. (2022) reveals functionally clustered representations of complex visual features in the lobular columnar neurons projecting to the central brain. In contrast, our work focuses on orientation selectivity in the earlier stages of the optic lobe, based on connectome-driven circuit structure. We will revise the manuscript to clarify how these complementary findings together highlight the presence of structured visual maps.\nResponse to Specific Questions:\n1.\tThe threshold was initially selected based on visual inspection during pilot analysis, aiming to balance fit quality and coverage. To justify this choice, we will include additional plots in the Appendix showing neurons across a range of R^2 and RSS_norm values, including examples near the threshold. \n2.\tAs noted above, our connectome-driven model consists of a total of 138,639 neurons and 1,508,983 synapses. We will clarify these numbers more explicitly in the manuscript.\n\nWe hope these clarifications address your concerns, and we’re grateful for the opportunity to strengthen the paper further."}, {"review_id": "Y0e8C2ZZYn", "reviewer": "Reviewer_mZ6e", "summary": "This 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.", "questions": "Please 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?", "limitations": "Yes. Subject to my questions in the weaknesses section above.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the thoughtful and constructive feedback. Below, we address each of the specific concerns:\n\n1.\tParameter robustness\n\nGood suggestion. Our model and parameters were taken from the LIF framework described in Shiu et al. (ref 45). To assess sensitivity to parameter perturbations, we performed a variation analysis where all parameters (except $V_{rest}$ and $V_{threshold}$) were randomly perturbed by a fixed percentage. At $\\pm$10% variation, we observed minimal changes in preferred orientation across neurons. To further probe robustness, at $\\pm$50% orientation selectivity was noticeably degraded; however, surprisingly, the ODEs (Eqns 1 and 2 in Appendix) remained stable and did not diverge. This suggests that the system retains a degree of functional robustness even under large perturbations. We will include results in the Appendix.\n\n2.\tSynaptic Weight Assignment\n\nThank you for flagging this ambiguity. This computational method is taken from the Shiu et al. paper (ref 45). To clarify, $w_{j,i}$ represents the number of annotated synapses from neuron $j$ to neuron $i$ in the FAFB connectome. Thus, if a connection has multiple synapses, the conductance update is scaled proportionally. \n\n3.\tEquation 3\n\nIndeed, for equation 3 of Appendix B should be $w_{syn} * w_{j,i}$. We apologise for the lack of clarity and will revise the manuscript to clarify this formulation.\n\n4.\tContribution Beyond Qualitative Predictions:\t\n\nWhile orientation selectivity has been qualitatively suggested in prior studies (e.g. Seung [ref 44]; Klapoetke et al. (2022)), these works either describe local orientation biases or propose conceptual models but do not demonstrate orientation maps. Our work differs in that we implement a spiking neural model derived entirely from the FAFB connectome without parameter tuning, and show that smooth orientation preference maps emerge naturally from the architecture. As detailed in lines 146-149, we further quantify the alignment between anatomical connectivity and simulated orientation preference, providing the first mechanistic and testable link between structure and function in this context.\n\n5.\tCross-Species Perspective:\n\n Our results suggest that orientation maps may not be exclusive to large-brained vertebrates, but arise from generic principles of connectivity and spatial organisation. The orientation maps in different species may have emerged independently across evolution, driven not by historical accident but by shared computational demands. E.g. trade-offs between wiring cost, spatial resolution, and the nature of downstream decoding. Further theoretical and computational studies are needed to reveal the underlying computational principles.\n\nWe hope these clarifications address your concerns, and we’re grateful for the opportunity to strengthen the paper further."}, {"review_id": "yF2X15wLjF", "reviewer": "Reviewer_RoyT", "summary": "The 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.", "questions": "1. 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.)", "limitations": "1. 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.", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I do not recommend acceptance. The authors have not addressed core concerns about periodicity, coverage vs. continuity, and properly reported map quantitative metrics. These are fundamental for the claim about orientation maps. Orientation maps consist of a repetitive layout of iso-orientation domains interspersed with singularities (pinwheel centers). Locally, they can approximate a hexagonal lattice, but global variations and irregular pinwheel placement make the pattern quasi-periodic. Early theory claims this as an optimization under coverage–continuity trade-offs. However, in the current manuscript, only a small parts of the map satisfy both coverage and continuity- these areas have pinwheel centers, which cover all orientations; other large parts exhibit continuity without full coverage- they only have some iso-orientation domains. If the map has periodic pinwheel centers and iso-orientation domains in the map, it can cover all orientations for feature representations. Otherwise, as in this manuscript, if there are numerous iso-orientation domains with few pinwheel centers that lack coverage, it implies that iso-orientation domains are “blind” to (can not represent) certain orientations in some part of the optic lobe.", "rebuttal": "Thank you for the feedback. We address the major concerns and specific questions below.\n\n1.\tAs shown in Appendix B.2 and Figure S1b, we assessed the robustness of the orientation map to moderate changes in input strength by scaling the stimulus parameters $A_x$ by -20%, which modulates the amplitude of upstream photoreceptor input. To directly address the reviewer’s concern, we extended this analysis by uniformly reducing the stimulus intensity ($A_x$) to 60% of its original value. In both cases, we observed that the preferred orientation map structure was preserved. The results in the latter case will be added to the revised paper. This demonstrates that the emergence of orientation tuning in our model is robust to input strength and does not critically depend on precise stimulus gain.\n2.\tRegarding whether the observed tuning qualifies as a true “orientation map”, we fully agree that classical orientation maps, particularly those found in mammals, are characterised by hallmark features such as pinwheel centres and quantitative metrics including pinwheel density. \nThat said, our intent was not to suggest that the maps we observe in Drosophila are directly equivalent in form or developmental origin to mammalian orientation maps. Rather,  we use the term “orientation maps” to describe a topographic organisation of orientation preferences, with local smooth gradients and singularities across spatially structured circuits. While Drosophila lacks a columnar architecture, we find an emergent structure in our model that shares conceptual features with cortical maps.\nTo directly address the reviewer’s concern, we extracted the analytical framework from Kaschube et al. (2010) and performed a quantitative assessment of our maps:\n-\tPinwheel centres were identified by calculating local topological charge from interpolated complex-valued orientation fields.\n-\tColumn spacing was estimated using spectral power analysis, yielding a dominant spatial frequency.\n-\tPinwheel density was then computed as the number of detected pinwheels per hypercolumn area, consistent with the definition as mentioned in the paper.\n-\tWe further evaluated nearest-neighbour distances (nn) between pinwheels, in line with the metrics from Ho et al. (2021), which mentions that primate brains have a universal design of nn ~0.35 for any pinwheel.\nThe results are as follows:\n\t- Dm layer: \n      \t\t - Pinwheel density = 3.63 per hypercolumn and nn = 0.28\n\t- Pm layer: \n         \t- Pinwheel density = 3.46 per hypercolumn and nn = 0.27\nWe acknowledge a key caveat where our maps are derived from simulated model responses and not from raw optical imaging or physiological recordings. As such, filtering methods may differ. Nevertheless, our analysis follows the quantitative structure and conceptual definition of the two referenced papers. Though the results may not follow strictly the universal criteria observed in mammalian cortex, we would like to highlight that our results emerge from a fundamentally different biological system.\n\n3.\tOur intent was not to claim a tested contribution to AI architecture, but rather to suggest that the emergence of functional maps from untrained, biologically constrained structure may provide conceptual inspiration for future models. We agree that this framing could be misinterpreted as implying empirical results, and we will remove this claim in the abstract.\n\nAnswers to questions:\n\n1.\tThank you for this correction, and we fully agree that Vita et al. (2024) demonstrate the presence of a location-dependent continuous orientation map in the mouse retina. Our statement of salt-and-pepper organisation with retinal architecture was incorrect in this context. We will revise the manuscript.\n\n2.\tWe will revise the manuscript to spell out acronyms at first use and use consistent abbreviations thereafter. We will also rephrase the second sentence of the abstract to more accurately reflect species differences in cortical organisation.\n\n3.\tAccording to connectomic reconstructions, L4 and L5 form reciprocal lateral loops with L2 and L1, respectively. These connections are columnar-local at the soma level but extend horizontally via their arborisations, supporting intrachannel lateral processing within the lamina. In our model, we included these interactions to preserve known feedforward and lateral motifs.\n\n4.\tYes, the distal medulla layer also includes lateral connections, primarily via Mi, Tm and Dm cell types, which arborise horizontally across neighbouring columns. These lateral interactions are supported by connectomic evidence from the FAFB dataset. \n\n5.\tWhile L1 and L3 are canonically classified as ON-pathway inputs due to their downstream targets, all three lamina neurons – L1, L2 and L3 are responsive to light decrements at the photoreceptor level and show OFF responses to contrast edges [ref 29]. In our model, the OFF-bar stimulus produces stronger local contrast and therefore elicits higher firing from these lamina neurons when the dark edge aligns with their receptive fields. This activity then propagates through the OFF-selective downstream medulla neurons (e.g. Tm1, Tm2, Tm4). In contrast, full-field brightening evokes more uniform activation, resulting in a broader but lower-amplitude population response. \n\n6.\tThe right panel of Figure 2c was originally intended to illustrate a neuron with poor orientation tuning. We agree that the absence of a fit curve may be misleading. To improve clarity, we will add the Gaussian fit to the figure.\n\n7.\tWe computed both the f-I and f-V curves of our LIF neuron model. The f-I curve was obtained by measuring firing rate in response to a constant input drive g, which acts analogously to injected current. The f-V curve was derived by varying the neuron’s target membrane potential v_0. Both curves exhibit a sharp threshold, below which firing is suppressed and a smooth suprathreshold region where firing increases. This defines a ReLU-like nonlinearity: zero firing below the threshold and a rising, slightly concave slope above it.\n\n8.\tYes, inhibitory synapses were included in the LIF network we used, which was taken from Shiu et al. (ref 45). Each neuron was assumed to be either fully excitatory or fully inhibitory. Inhibitory synapses were modelled using the same conductance-based LIF framework as excitatory, but with negative synaptic weights and a reversal potential set to mimic hyperpolarising inhibitory drive. Examples of inhibitory neuron types include C2, C3, Mi4, and Dm8.\n\n9.\t“Total Neurons” refers to the number of neurons of each type that were included in the simulation and received visual input. This includes all modelled neurons of that type, regardless of whether they exhibited orientation tuning. We will revise the figure caption.\n\n10.\tOur simulations are based on the static adult FAFB connectome and do not incorporate developmental or activity-dependent changes in connectivity. Our results show that orientation tuning and map-like structure can emerge from anatomical wiring alone, without plasticity. While not perfectly periodic, the model produces smooth, reproducible maps with local clustering that is consistent with biological variability in the Drosophila optic lobe.\n\n11.\tWe follow the convention used in Seung’s paper (ref 44), where orientation is defined relative to the vertical axis. Under this convention, 0° corresponds to a vertical bar and 90° to a horizontal bar.\t\n\n12.\tOur current model operates directly on the adult connectome and does not include synthetic interpolation of neurons. Simulating increased medulla density while preserving biologically plausible connectivity would require a principled generative model, which we consider an important next step.\n\n13.\tThank you for the suggestion. We computed the average ratio of active neurons across different oriented bar stimuli as a proxy for population sparse coding. Specifically, we defined a neuron as “active” if its firing rate exceeded 15Hz, and calculated the fraction of active visual neurons for each orientation. We found that approximately 33-34% of neurons were present on average across orientations, with relatively minor variation. This suggests that the population response is moderately sparse and that sparse coding does not vary strongly with stimulus orientation in this dataset. \n\n14.\tGreat question! But we don’t have a clear answer yet. Maybe, organising orientation preference topographically could facilitate local integration and circuit reuse, reducing wiring cost while enabling modular computation, similar to the advantages proposed in mammalian systems.\n\nTo address the questions raised in the limitations sections:\n\n1.\tOur intent was not to imply that the emergent orientation maps themselves encode motion per se, but rather that they may support pre-processing for motion computation, such as contour alignment or edge flow analysis, functions that are integral to motion-sensitive circuits downstream. For instance, direction-selective neurons like T4 and T5 are orientation-selective as well. [ref 16]\n\n2.\tWe agree that the phrasing is too broad without supporting evidence; thus, we will remove this sentence.\n\n3.\tWe performed targeted ablation studies to probe the feedforward contributions to orientation selectivity. Silencing the Tm cells led to an 89.6% reduction in well-fit orientation-selective neurons in the Pm layer, while the Dm layer showed only a 9.3% reduction. In contrast, silencing Mi neurons resulted in a dramatic loss of orientation tuning across both layers, with fewer than 5% of well-fit neurons remaining in either the Dm or Pm layers. These findings suggest that while Tm neurons provide critical convergence specifically to Pm cells, Mi neurons are essential upstream contributors to orientation selectivity more broadly. We will incorporate this into the revised manuscript.\n\nWe hope these clarifications address your concerns, and we’re grateful for the opportunity to strengthen the paper further."}, {"review_id": "XbTOMs5nyQ", "reviewer": "Reviewer_HGht", "summary": "This 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.", "questions": "Questions:\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?", "limitations": "Yes, the authors adequately provided the limitations and potential negative societal impact of their work.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "I recommend accept.", "rebuttal": "Thank you for the thoughtful comments and constructive feedback. Below, we address your concerns.\n\n1.\tWe acknowledge the assumption of neuronal homogeneity. Our model, taken from Shiu et al. (ref 45), allows us to isolate the role of anatomical structure in shaping function. While it simplifies biological variability, it serves as a controlled baseline to test whether connectivity alone can drive orientation selectivity.\n\n2.\tOur connectome-driven model consists of a total of 138,639 neurons and 1,508,983 synapses (as in the connectome dataset). We will clarify these numbers more explicitly in the manuscript.\n\n3.\tTo clarify, the values of A_x and B_x were not obtained through rigorous data fitting. Rather, they reflect qualitative trends reported in prior calcium imaging studies (Ketkar et al. [ref 29]), such as the relative sensitivity of L1 and L3 neurons. Given the absence of absolute baseline fluorescence values, we chose integer values to preserve relative scaling while simplifying implementation. As shown in Appendix B.2 and Figure S1b, the model’s orientation tuning is robust to moderate changes in these parameters, indicating that our main results do not hinge on their precise values.\n\n4.\tRegarding the input fit quality, we assessed the quality of the input model by evaluating known ON/OFF response patterns in downstream neurons, as detailed in Appendix B.2. Specifically, OFF-type cells (e.g. Tm1, Tm2, Tm4) and ON-type cells (e.g. Mi1, Mi4, Tm3) demonstrated firing patterns that align with established physiological responses to visual stimuli (Figures S1d, e). These patterns validate that the input model reasonably captures key features of visual processing. \n\nWe hope that these clarifications address the reviewer’s concerns, and we thank the reviewer again for helping us improve the clarity and rigour of the manuscript."}, {"review_id": "dsPpC3QEJP", "reviewer": "Reviewer_oKVH", "summary": "The 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.", "questions": "One 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?", "limitations": "One 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.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "The responses to my concerns were clearly expressed, and I keep my score.", "rebuttal": "Thank you for the thoughtful and encouraging feedback. Below, we address your suggestions and questions.\n\n1.\tWe quantified synaptic connectivity by plotting each synapse (p_i, q_i) as a point (θ_pi, θ_qi), where θ denotes the preferred orientation (in degrees) of the pre- and postsynaptic neurons. The resulting 2D heatmap reveals network structure where in the excitatory network, neurons with similar orientation preferences are more likely to be recurrently connected, consistent with topographic organisation and colinear facilitation as proposed in Seung’s paper (ref 44). Inhibitory connections show distinct off-diagonal structure, reflecting more specific circuit motifs. These results support the idea that recurrent networks in the optic lobe preferentially link neurons with similar selectivity. We will add this analysis to the Appendix section in the revised manuscript.\n\n2.\tIn this work, we focus on orientation tuning and do not directly address the organisation of direction-selective circuits. Nonetheless, the presence of a structured orientation map within Dm/Pm does not preclude the existence of a superimposed motion map. The aperture problem implies that orientation and direction are linked geometrically, but whether these are represented independently or jointly remains an open question. \n\n3.\tIndeed, we observed a preference for about 90° in Dm15. We hypothesise that Dm15 might be biologically specialised to detect horizontal orientations (we used the convention of Seung’s paper (ref 44), where 90° corresponds to a horizontal bar), possibly reflecting an ecological need to process horizons or ground-level cues. This fits with broader directional asymmetries observed in the optic lobe, where certain circuits preferentially process cardinal orientations.\n\nThank you for the insightful comments, and we are grateful for the opportunity to strengthen the paper further."}]

Author rebuttal:

Meta-review: This paper simulates orientation maps in Drosophila optic lobe using the full measured brain connectome of Drosophila and networks of simulated spiking neurons. In a computational modeling effort, they address an interesting open problem in neuroscience: whether topographic orientation maps, which have been mainly observed in mammalian visual systems, require cortical structures or can also emerge in species without cortical structure. The reviewers recognized several significant strengths in this work, including very thorough experiments and analysis, and the compelling approach of connecting connectome data to functional observations. The modeling work provides valuable insights suggesting that topographic orientation maps do not necessarily require cortical structures and can emerge across species with large phylogenetic distances.

The reviewers initially raised a few concerns such as the sensitivity of results to parameters and ambiguities in parameter selection. However, these issues appear to have been adequately addressed in the authors' rebuttals. One reviewer also raised concerns regarding differences between the structure of simulated orientation maps in Drosophila compared to those observed in mammalian visual systems. While the authors acknowledged these differences, they should address them more clearly in the camera-ready version. Nevertheless, I believe these cross-species differences could provide interesting experimental predictions for future comparative studies.

Overall, this work makes a solid contribution to computational neuroscience by addressing a fundamental question about visual system organization across species. The thorough experimental approach and successful bridging of structural and functional data outweigh the remaining concerns. Therefore, I recommend acceptance.
Decision: Accept (poster)