Two Methods to Improve the Efficacy of ReSuMe

Abstract

Neuron learning is the basis of the more complex learning of neuron network. Resume is the one of the most popular used supervised learning algorithm for spiking neurons. It corresponds to the Widrow-Hoff rule and its weights adjustment is derived from the spike-based Hebbian processes. Although it makes quite success the learning accuracy decreases quickly when the desired output spike train gets longer. This paper analyzes two important factors related to the learning convergence of Resume. And we proposed two methods based on them to improve the efficacy of Resume. The experimental results show that the two improved algorithms both can achieve better performance.