Analysis of the Effects of Decay Coefficient and Time Resolution in SNN Backpropagation

Abstract

High-performance neural networks operating at low power use spiking neural network (SNN) that are biologically closer than traditional ANN. SNN, unlike ANN, receives a series of binary-coded spike trains as input and updates the membrane potential of the neuron and generates spikes over a period of time specified by the number of spike trains. The function that generates the spike corresponding to the activation function of the ANN is not differentiable, which makes it difficult to apply the backpropagation (BP) algorithm used in the ANN. In order to overcome this problem, studies using numerical approximation of derivatives have been carried out in various ways. However, research on the decay coefficient and the number of spike trains, which are characteristic of SNN neuron, are insufficient. In this paper, we analyze the distribution of spikes and discuss how the decay coefficient characteristics of neurons and the number of spike trains affect network performance.