Connectome Wiring Shapes Motor Lesion Phenotypes in Embodied Drosophila Locomotion

A central controversy in computational neuroscience—recently formalized as The Digital Sphinx debate (Brunton,Abe, Hu, & Tuthill, 2026)—asserts that deep reinforcement learning (DRL) can optimize arbitrary artificial neural networktopologies to generate realistic animal locomotion. Consequently, high-level behavioral replication alone is insufficient toprove that connectome-constrained models capture genuine biological representations. Here, we present a pre-registeredadversarial lesion audit testing whether biological synaptic connectivity produces functional motor specificity thatdegree-matched random networks cannot replicate under identical embodied biomechanics. Using the Drosophilamelanogaster Janelia MaleCNS v1.0 connectome, we mapped the descending motor subcircuit (4 DN → 125 VNCInterneurons → 377 Motor Neurons) controlling 42 leg joint degrees of freedom in FlyGym MuJoCo physics. We generatedan ensemble of 20 degree-preserving bipartite null models using Markov-chain double edge swaps (0.0 degree deviation;~7% synaptic overlap) and evaluated N = 10 independently-seeded biological policy instances against these 20 nullmodels under targeted in-silico unilateral ablation of descending steering neuron DNa01. Unilateral DNa01 ablation inbiological connectome models induced a stereotyped lateralized steering turn of 38.19 ± 10.83°/s (95% bootstrap CI:[32.37, 45.22]°/s), consistent with in-vivo behavioral optogenetic perturbations of DNa01 (Rayshubskiy et al., 2025). Incontrast, identical single-neuron ablations in the 20 degree-matched rewired null models yielded an average turning rate ofonly 2.92 ± 1.25°/s (95% bootstrap CI: [2.12, 3.79]°/s), with 0 of 20 null models reproducing the turning phenotype. Thesenon-overlapping distributions confirm our pre-registered falsification criterion and demonstrate that while unconstrainedoptimization can make arbitrary network graphs walk, only the authentic biological wiring diagram preserves single-celllesion vulnerability and localized motor control.Code: https://github.com/Anishp-cell/connectome-drl

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-28
DOI
https://doi.org/10.5281/zenodo.23018265
Primary Topic
Neurobiology and Insect Physiology Research
Type
preprint
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Connectome Wiring Shapes Motor Lesion Phenotypes in Embodied Drosophila Locomotion

Anish Pathak
Zenodo (CERN European Organization for Nuclear Research)
Neurobiology and Insect Physiology Research
preprint

Connectome Wiring Shapes Motor Lesion Phenotypes in Embodied Drosophila Locomotion

Anish Pathak
preprint en

Abstract

A central controversy in computational neuroscience—recently formalized as The Digital Sphinx debate (Brunton,Abe, Hu, & Tuthill, 2026)—asserts that deep reinforcement learning (DRL) can optimize arbitrary artificial neural networktopologies to generate realistic animal locomotion. Consequently, high-level behavioral replication alone is insufficient toprove that connectome-constrained models capture genuine biological representations. Here, we present a pre-registeredadversarial lesion audit testing whether biological synaptic connectivity produces functional motor specificity thatdegree-matched random networks cannot replicate under identical embodied biomechanics. Using the Drosophilamelanogaster Janelia MaleCNS v1.0 connectome, we mapped the descending motor subcircuit (4 DN → 125 VNCInterneurons → 377 Motor Neurons) controlling 42 leg joint degrees of freedom in FlyGym MuJoCo physics. We generatedan ensemble of 20 degree-preserving bipartite null models using Markov-chain double edge swaps (0.0 degree deviation;~7% synaptic overlap) and evaluated N = 10 independently-seeded biological policy instances against these 20 nullmodels under targeted in-silico unilateral ablation of descending steering neuron DNa01. Unilateral DNa01 ablation inbiological connectome models induced a stereotyped lateralized steering turn of 38.19 ± 10.83°/s (95% bootstrap CI:[32.37, 45.22]°/s), consistent with in-vivo behavioral optogenetic perturbations of DNa01 (Rayshubskiy et al., 2025). Incontrast, identical single-neuron ablations in the 20 degree-matched rewired null models yielded an average turning rate ofonly 2.92 ± 1.25°/s (95% bootstrap CI: [2.12, 3.79]°/s), with 0 of 20 null models reproducing the turning phenotype. Thesenon-overlapping distributions confirm our pre-registered falsification criterion and demonstrate that while unconstrainedoptimization can make arbitrary network graphs walk, only the authentic biological wiring diagram preserves single-celllesion vulnerability and localized motor control.Code: https://github.com/Anishp-cell/connectome-drl

Zenodo (CERN European Organization for Nuclear Research)
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Neurobiology and Insect Physiology Research
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Connectome Wiring Shapes Motor Lesion Phenotypes in Embodied Drosophila Locomotion — Anish Pathak · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS