Deep Spiking Neural Network Using Spatio-temporal Backpropagation with Variable Resistance

Abstract

In recent years, the learning of deep spiking neural networks(SNN) has attracted increasing researchers’ interest, and has also made important progresses in theories and applications. It is desired to choose a neuron model with biological features and suitable for SNN training. Currently, Leaky Integrate-and-Fire(LIF) model is mainly used in deep SNN and some factors that can express the spatio-temporal information are ignored in the model. In this work, inspired by the Hodgkin-Huxley(H-H) model, we propose an improved LIF neuron model, which is an iterative current-based LIF model with voltage-based variable resistance. The improved neuron model is closer to the characteristics of the biological neuron model, which can make use of the spatio-temporal information. We further construct a new SNN learning algorithm that uses spatio-temporal back propagation by defining a loss function. We evaluated the proposed methods on single-label and multi-label data sets. The experimental results show that the variable resistance of the neuron model will affect the performance of the model. Choosing the appropriate relationship between the variable resistance and the membrane voltage can effectively improve the recognition accuracy.