2025-10-22 スイス連邦工科大学ローザンヌ校(EPFL)
The larval zebrafish robot, Zbot. 2025 BioRob EPFL CC BY SA 4.0
<関連情報>
- https://actu.epfl.ch/news/roboticists-reverse-engineer-zebrafish-navigatio-2/
- https://www.science.org/doi/10.1126/scirobotics.adv4408
人工具現化回路が脊椎動物の視覚運動行動の神経構造を明らかにする Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors
Xiangxiao Liu, Matthew D. Loring, Luca Zunino, Kaitlyn E. Fouke, […] , and Eva A. Naumann
Science Robotics Published:15 Oct 2025
DOI:https://doi.org/10.1126/scirobotics.adv4408
Abstract
Brains evolve within specific sensory and physical environments, yet neuroscience has traditionally focused on studying neural circuits in isolation. Understanding of their function requires integrative brain-body testing in realistic contexts. To investigate the neural and biomechanical mechanisms of sensorimotor transformations, we constructed realistic neuromechanical simulations (simZFish) of the zebrafish optomotor response, a visual stabilization behavior. By computationally reproducing the body mechanics, physical body-water interactions, hydrodynamics, visual environments, and experimentally derived neural network architectures, we closely replicated the behavior of real larval zebrafish. Through systematic manipulation of physiological and circuit connectivity features, impossible in biological experiments, we demonstrate how embodiment shapes neural activity, circuit architecture, and behavior. Changing lens properties and retinal connectivity revealed why the lower posterior visual field drives optimal optomotor responses in the simZFish, explaining receptive field properties observed in real zebrafish. When challenged with novel visual stimuli, the simZFish predicted previously unknown neuronal response types, which we identified via two-photon calcium imaging in the live brains of real zebrafish and incorporated to update the simZFish neural network. In virtual rivers, the simZFish performed rheotaxis autonomously by using current-induced optic flow patterns as navigational cues, compensating for the simulated water flow. Last, experiments with a physical robot (ZBot) validated the role of embodied sensorimotor circuits in maintaining position in a real river with complex fluid dynamics and visual environments. By iterating between simulations, behavioral observations, neural imaging, and robotic testing, we demonstrate the power of integrative approaches to investigating sensorimotor processing, providing insights into embodied neural circuit functions.
