用果蝇全脑连接图构建神经控制器，实现仿生运动控制
Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly
- 将果蝇全脑连接组直接转为图结构神经控制器
- 在多种运动任务中表现稳定，采样效率优于基线模型
- 适合研究生物启发智能与神经机械系统协同
动物的协调全身运动由脑区广泛连接的神经系统控制。全脑神经连接图谱（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被充分探索。本文提出果蝇连接组图模型（Fly-connectomic Graph Model），通过深度强化学习，将成年果蝇全脑连接组直接转化为仿真果蝇的图结构神经控制器，实现了多样化的运动任务稳定控制。相比图结构与非图结构基线，该模型表现出更优的样本效率。结果表明，将全脑布线原则转化为可执行的架构先验，不仅提升了控制策略设计的有效性，还增强了动态信息流的可解释性。本工作为连接神经力学与具身智能提供了计算平台，推动了更贴近自然的智能系统发展。
原文摘要 · Abstract (English)
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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