Learning and Memory in Living Neuronal Networks Connected to Moving Robot

Abstract

Dissociated culture system with multi-electrode array is fully useful lor elucidation of network dynamics of neurons. We have already found that the spatio-temporal pattern of spontaneous activities are enough to construct dynamic functional assemblies of neurons, and synaptic potentiation can induce re-organization of such assemblies of neurons. The living neuronal network can obtain a kind of learning and memory, i.e., intelligence by interactions with outer world. In this paper, we introduce Biologically Inspired Model as a fuzzy computational system for living neuronal networks connected to moving robot, Khepera II robot. The system has a loop procedure, the top-down bio-processing for sending actuator signals to robot from living neuronal network, and the bottom-up robot-processing lor electrical stimulation to living neuronal network from robot. By applying the biologically inspired model to the obstacle problem and the tracking problem of robot, we are analyzing the Interaction and plasticity of living neuronal network connected to moving robot, and we discuss reconstruction of the neuronal network, which can process thinking.