arXiv:2602.17997v3 Announce Type: replace Abstract: 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
The convergence of advanced computational neuroscience and AI research is enabling unprecedented progress in modeling biological neural networks for artificial control systems.
This research provides a fundamental breakthrough in understanding biological intelligence and offers a novel paradigm for designing highly efficient and adaptable AI controllers for embodied agents.
The direct instantiation of whole-brain connectomes as AI controllers shifts the approach from abstract neural networks to biologically inspired architectures, potentially leading to more robust and complex AI systems.
- · AI researchers
- · Robotics industry
- · Computational neuroscience
- · Biomedical engineering
- · Traditional AI control system developers relying solely on deep learning
Further research will focus on scaling this connectomic modeling approach to more complex organisms and tasks.
This could lead to a new generation of robots with unprecedented biological-level agility, adaptability, and learning capabilities.
The deeper understanding of biological intelligence derived from these models may inform the development of human-like artificial intelligence and even prosthetic interfaces.
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Read at arXiv cs.LG