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

2026-02-20

AI Summary

This study investigates the direct use of a real biological connectome as a neural network controller for whole-body motor control. Leveraging the complete brain connectome of Drosophila, the authors construct FlyGM—a directed message-passing graph neural network—and integrate it with a biomechanical body model to perform diverse locomotion tasks without task-specific architectural modifications. This work presents the first demonstration of an intact biological brain connectome serving as a policy network in reinforcement learning, revealing its inherent structural advantages for embodied control. Experimental results show that FlyGM significantly outperforms degree-preserving rewired graphs, random graphs, and multilayer perceptrons across multiple locomotion tasks, achieving superior sample efficiency and control performance.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Bio-inspired Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• whole-brain connectome
• locomotion control
• embodied reinforcement learning
• neural architecture
• fruit fly

Innovation

• connectome
• graph neural network
• embodied reinforcement learning
• Drosophila
• whole-brain model