Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fly
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This is a machine learning research paper about using fruit fly brain connectomes as neural network architectures for locomotion control in reinforcement learning, not a security article.
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[2602.17997] Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly Support arXiv on Cornell Giving Day! We're celebrating 35 years of open science - with YOUR support! Your generosity has helped arXiv thrive for three and a half decades. Give today to help keep science open for ALL for many years to come. Donate! --> Computer Science > Machine Learning arXiv:2602.17997 (cs) [Submitted on 20 Feb 2026 ( v1 ), last revised 8 Mar 2026 (this version, v2)] 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 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. Subjects: Machine Learning (cs.LG) ; Robotics (cs.RO) Cite as: arXiv:2602.17997 [cs.LG] (or arXiv:2602.17997v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.17997 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zehao Jin [ view email ] [v1] Fri, 20 Feb 2026 05:09:28 UTC (6,173 KB) [v2] Sun, 8 Mar 2026 23:37:31 UTC (6,173 KB) Full-text links: Access Paper: 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) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-02 Change to browse by: cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )