Short-term depression of a cultured neuronal networks in order to generate collision avoidance behavior of the neurorobot.

Abstract

Synaptic plasticity in neuronal networks has been shown to be important for motor learning. In this study, we analyzed the neuronal network that interacts with the outside world through the robot body to induce collision avoidance behavior of this neuro-robot, by reducing the number of spikes in spontaneous electrical activity due to the short-term depression induced by repeated electrical inputs to the cultured neuronal network. When the robot detects an obstacle, it is designed to avoid collision with the obstacle by slowing down its motor speed in response to the short-term depression (STD) expressed in a subset region of the network, induced by continuous inputs to a certain stimulatiuon electrode corresponding to the position of the obstacle. We analyzed the STD effects by the stimulus patterns and performed a running experiment of the neurorobot with the optimal stimulus condition (10 Hz). As a result, the robot succeeded in expressing the collision avoidance behavior. It was observed that the turning behavior increased. The spike frequency of spontaneous electrical activity before and after the running experiment tended to decrease, and the periodic intervals between spike firings tended to be larger than those before the experiment. These results suggest that the running of the neurorobot with avoiding obstacles induces long-term depression (LTD) in the cultured neural network and increases the interval width of the firing cycle of spontaneous neuroelectric activity.