An Efficient non-Backpropagation Method for Training Spiking Neural Networks

Abstract

Spiking Neural Networks (SNNs) have recently attracted significant research interest and have been regarded as the next generation of artificial neural networks due to their suitability for energy-efficient event-driven neuromorphic computing. However, the existing SNNs error backpropagation (BP) method may have severe difficulties in non-differentiable spiking generation functions and vanishing or exploding gradients. In this paper, we introduce an efficient method for training SNNs without backpropagation. The information bottleneck (IB) principle is leveraged to learn synaptic weights and neuron thresholds of an SNN. The membrane potential state for information representation is learned in real-time for higher time and space efficiency compared with the conventional BP method. Experimental results show that the proposed biologically plausible method achieves comparable accuracy and considerable steps/memory reduction in training SNN on MNIST/FashionMNIST datasets.