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.

翻译：动物在由全脑连接塑造的神经系统控制下进行协调的全身运动。全脑神经连接的映射（即连接组）为建模感觉运动信息流提供了天然图结构，但其作为具身智能体神经控制器的潜力尚未得到充分探索。本文提出果蝇连接组图模型，该模型通过深度强化学习将成年果蝇全脑连接组直接实例化为图形化神经控制器，用于驱动仿生机械果蝇的运动模拟。我们在多种运动任务中实现了稳定性能，并在样本效率上优于图基和非图基线方法。研究结果表明，通过将全脑布线原理转化为可操作的架构先验，可形成一种符合生物学规律的有效控制策略设计方法，同时通过动态信息流提升模型可解释性。本研究通过提供计算平台以探究动物行为背后的感觉运动变换机制，并建立推动更贴近自然智能系统发展的范式，凸显了桥接神经力学与具身智能的潜力。