Using Spiking Neural Networks for the Generation of Coordinated Action Sequences in Robots

SNNs have been tested as possible candidates for the implementation of robot controllers, in particular behaviour based controllers. but in most approaches their real power, related to their inherent temporal processing, and, especially, temporal pattern generating capabilities, have been ignored. This paper is concerned with showing how SNNs in their most dynamic form can be easily evolved to provide the adaptable or sensor and context modulated pattern generating capabilities required for the generation of action sequences in robots. In fact, the objective is to have a structure that can provide a sequence of actions or a periodic pattern that extends in time from a very time limited sensorial cue.