Self-Organizing Digital Spike Maps for Learning of Spike-Trains

Abstract

This paper presents a digital spike map and its learning algorithm of spike-trains. The map is characterized by a swarm of particles on lattice points. As a teacher signal is applied, the algorithm finds a winner particle. The winner and its neighbor particles move in a similar way to the self-organizing maps. A new particle can born and the particle swarm can grow depending on the property of teacher signals. If learning parameters are selected suitably, the map can evolve to approximate a class of teacher signals. Performing basic numerical experiments, the algorithm efficiency is confirmed.