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

Abstract

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.