Optogenetically enhanced physical reservoir computing with <i>in vitro</i> neural networks for obstacle avoidance.

ABSTRACT:

Significance

The effects of optogenetic stimulation (OS) on in vitro 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

We aim to utilize optogenetically controlled in vitro neural networks and the first-order reduced and controlled error (FORCE) learning algorithm to achieve obstacle avoidance in neuro-robotic systems.<

SUBMITTER: Deng Y 

PROVIDER: S-EPMC12543164 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature