Evolving spiking neural networks for robot control

We describe a sequence of experiments in which a robot “brain” was evolved to mimic the behaviours captured under control of a heuristic rule program (imitation learning). The task was light-seeking while avoiding obstacles using binocular light sensors and a trio of IR proximity sensors. The “brain” was a spiking neural network simulator whose parameters were tuned by a genetic algorithm, where fitness was assessed by the closeness to target output spike trains. Spike trains were frequency encoded. The network topology was manually designed, and then modified in response to observed difficulties during evolution. We noted that good performance seems best approached by judicious mixing of excitation and inhibition. Besides robotic applications, the domain of “smart” prosthetics also appears promising