Whole-brain biological neural networks naturally support the learning and control of whole-body movements. However, the use of brain connectomes as neural network controllers in embodied reinforcement learning remains unexplored. We investigate using the exact neural architecture of an adult fruit fly's brain for the control of its body movement. We develop Fly-connectomic Graph Model (FlyGM), whose static structure is identical to the complete connectome of an adult Drosophila for whole-body locomotion control. To perform dynamical control, FlyGM represents the static connectome as a directed message-passing graph to impose a biologically grounded information flow from sensory inputs to motor outputs. Integrated with a biomechanical fruit fly model, our method achieves stable control across diverse locomotion tasks without task-specific architectural tuning. To verify the structural advantages of the connectome-based model, we compare it against a degree-preserving rewired graph, a random graph, and multilayer perceptrons, showing that FlyGM yields higher sample efficiency and superior performance. This work demonstrates that static brain connectomes can be transformed to instantiate effective neural policy for embodied learning of movement control.

翻译：全脑生物神经网络天然支持全身运动的学习与控制。然而，将大脑连接组作为具身强化学习中的神经网络控制器尚未得到探索。本研究利用成年果蝇大脑的精确神经结构控制其躯体运动。我们开发了果蝇连接组图模型（FlyGM），其静态结构与成年黑腹果蝇的完整连接组完全相同，用于全身运动控制。为实现动态控制，FlyGM将静态连接组表示为有向消息传递图，以建立从感觉输入到运动输出的生物学基础信息流。通过与生物力学果蝇模型集成，我们的方法在多种运动任务中实现了稳定控制，且无需针对特定任务进行架构调整。为验证基于连接组模型的结构优势，我们将其与度保持重连图、随机图及多层感知机进行对比，结果表明FlyGM具有更高的样本效率和更优的性能。本研究表明，静态大脑连接组可转化为有效的神经策略实例，用于运动控制的具身学习。