A Neuro-robot System Generating Collision Avoidance Behavior Using Short-term Depression Effects in a Cultured Neuronal Network

Synaptic plasticity in a neuronal network 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 generating collision avoidance behavior of this neuro-robot, by reducing the number of spikes in spontaneous electrical activity due to the short-term depression (STD) induced by repeated electrical inputs to the cultured neuronal network. We analyzed the STD effects by the stimulus patterns and performed a running experiment of the neuro-robot with the optimal stimulus condition (10 Hz). As a result, the robot successfully performed the collision avoidance behavior. During repeated experiments, increased turning behavior was observed. The spike frequency of spontaneous electrical activity tended to decrease after the running experiment, and the periodic intervals between spike firings tended to be larger than those before the experiment. These results suggest that the running of the neuro-robot with avoiding obstacles induces long-term depression (LTD) also in the cultured neuronal network and increases the interval of the firing cycle of spontaneous neuroelectric activity.