Spike-Time Dependant Plasticity in a Spiking Neural Network for Robot Path Planning.

Abstract

Abstract. This paper will present a path planning technique for autonomous mobile robot, based on the representation of the environment as a cognitive map through a spiking neural network (SNN) of O’Keefe place cells. The method is based on the concept of the travelling wave. For this purpose, we use a biologically plausible neural model (Izhikevitch model) which is the medium of a travelling wavefront stabilized by the Spike-Time Dependant Plasticity (STDP) process. The obstacles are represented by inhibited neurons and the robot by the unique externally excited place cell that initiates the wave. This method produces a gradient map that allows fast and reliable calculation of a feasible path.