In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot. We also examine the relation between neural dynamics in SNN and the robot behavior. First, we formulated a SNN model whose inputs and outputs were analog. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use dependent synaptic potentiation and depotentiation was formulated and implemented into the robot. In the environment, the robot gradually organized the network and the given tasks were successfully performed. The time needed for the training using self-organization method was much less than with genetic evolution, approximately one fifth.