Spike train learning algorithm, applications, and analysis

Abstract

An algorithm for spike train mapping and learning using spiking neural network (SNN) is introduced. Analysis of the proposed spike train mapping-learning algorithm provides a method for selecting appropriate mapping and learning parameters such as neuron threshold and mapping time window. An application is used to show the robustness of the learning algorithm. A comparison of the proposed SNN learning algorithm with the back-propagation learning algorithm for a classification problem is presented