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arXiv · 2602.17997

Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly

Zehao Jin·Yaoye Zhu·Chen Zhang·Yanan Sui
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Abstract

Animals perform coordinated whole-body movements under the control of neural systems shaped by brain-wide connectivity. The mapping of the whole-brain neural connections, or the connectomes, provides a natural graph for modeling sensorimotor information flow, yet its potential as a neural controller for embodied agents remains largely unexplored. Here, we introduce the Fly-connectomic Graph Model, which directly instantiates the whole-brain connectome of an adult Drosophila as a graph-structured neural controller for movements of a simulated biomechanical fruit fly via deep reinforcement learning. We achieve stable performance across diverse locomotion tasks, as well as better sample efficiency compared to both graph and non-graph baselines. Our results demonstrate a biologically informed way towards effective control policy design by translating whole-brain wiring principles into actionable architectural priors, while also improving the interpretability through dynamic information flow. This work also highlights the potential to bridge neuromechanics with embodied intelligence by providing a computational platform for investigating the sensorimotor transformation underlying animal behavior and a paradigm to advance the development of more nature-aligned intelligent systems.

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Zehao Jin, Yaoye Zhu, Chen Zhang, Yanan Sui. 2026-02-20. Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly. https://arxiv.org/abs/2602.17997

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