Self-organization of spiking neural network generating autonomous behavior in a real mobile robot

In this paper, we study the relation between neural dynamics and robot behavior to develop self-organization algorithm of spiking neural network applicable to autonomous robot. We first formulated a spiking neural network model whose inputs and outputs were analog. We then implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task exists with the spiking neural network, the robot was evolved with the genetic algorithm (GA) in an environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. Then, a self-organization algorithm based on the use-dependent synoptic potentiation and depotentiation was formulated and implemented into the robot. In the environment, the robot gradually organized the network and the obstacle avoidance behavior was formed. The time needed for the training was much less than with genetic evolution, approximately one fifth (1/5).