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[2602.17997] Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly
Summary
The article presents a novel approach to locomotion control in fruit flies using a whole-brain connectomic graph model, demonstrating enhanced efficiency in movement control through biologically grounded neural networks.
Why It Matters
This research bridges neuroscience and robotics, offering insights into how biological neural architectures can inform artificial intelligence systems. By utilizing the connectome of a fruit fly, the study paves the way for more efficient and adaptable robotic movement control, which could have broader implications in robotics and AI development.
Key Takeaways
- The Fly-connectomic Graph Model (FlyGM) mimics the neural structure of a fruit fly's brain for movement control.
- FlyGM shows superior performance and sample efficiency compared to traditional neural network architectures.
- The study highlights the potential of using biological connectomes in embodied reinforcement learning.
Computer Science > Machine Learning arXiv:2602.17997 (cs) [Submitted on 20 Feb 2026] Title:Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly Authors:Zehao Jin, Yaoye Zhu, Chen Zhang, Yanan Sui View a PDF of the paper titled Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly, by Zehao Jin and 3 other authors View PDF HTML (experimental) 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 samp...
