Optimization of Output Spike Train Encoding for a Spiking Neuron Based on its Spatio-Temporal Input Pattern

Abstract

A common learning task for a spiking neuron is to map a spatio-Temporal input pattern to a target output spike train. There is no prescribed method for selection of the target output spike train. However, the precise spiking pattern of the target output spike train (output encoding) can affect the learning performance of the spiking neuron. Therefore, systematic methods of finding the optimum spiking pattern for a target output spike train that can be learned by spiking neurons are needed. Here, a method is proposed to adaptively adjust an initial suboptimal output encoding during different learning epochs to find the optimal output encoding. A time varying value of a local event called a spike trace is used to calculate the amount of a required adjustment. The remote supervised method (ReSuMe) learning algorithm is used to train the weights, and the proposed method is used for finding optimized output encoding (optimized desired spikes). Experimental results show that optimizing the output encoding during the learning phase increases the accuracy. The proposed method was applied to find optimized output encoding in classification tasks and the results revealed improvements up to 16.5% in accuracy compared to when using the non-Adapted method. It also increases the accuracy in a classification task from 90% to 100%.