Optogenetically enhanced physical reservoir computing with in vitro neural networks for obstacle avoidance

作者：Yin Deng, Jie Li, Yarong Lin, Zeying Lu, Lili Gui, Longze Sha, Xiaojuan Sun, Yueheng Lan, Qi Xu, Kun Xu · 发表于：Journal of Biomedical Optics · 年份：2025 · DOI：10.1117/1.jbo.30.10.105004 · 被引用次数：2 · 研究领域：Neural Networks and Reservoir Computing、Photoreceptor and optogenetics research、Advanced Memory and Neural Computing

Significance: neural network behavior were studied through a reservoir computing-based obstacle avoidance task, revealing its impact on the task-processing capabilities of the network. Furthermore, it is demonstrated that a minimal output of signals from 15 neurons in the network is sufficient to achieve stable task control, with a success rate exceeding 95%. The optogenetically enhanced biological reservoir computing frame could find applications in neuro-robotic control and brain-inspired intelligence. Aim: neural networks and the first-order reduced and controlled error (FORCE) learning algorithm to achieve obstacle avoidance in neuro-robotic systems. Approach: We presented an all-optical biological reservoir computing framework that leverages optogenetics and calcium imaging to precisely regulate and record neuronal activities. A closed-loop system was developed incorporating the FORCE learning algorithm, which guided a virtual car through obstacle avoidance tasks. Results: of training. OS significantly improved the obstacle avoidance success rate, enhancing the system's adaptability and accuracy. Conclusions: The results highlight the potential of optogenetically controlled biological neural networks in neuro-robotic systems, showcasing their capability to achieve accurate and efficient obstacle avoidance through physical reservoir computing.