An Overview of Spikingneural Networks

Abstract

In recent years, Artificial neural network has made great progress in image, machine perception and other aspects, and has a very good performance in the scope of deep learning. As a highly intensive neural network, Artificial neural network's performance has gradually reached saturation in today's increasing network demand, but its efficiency and consumption are still relatively large. Therefore, more and more attention has been paid to the peak neural network with low energy consumption in operating equipment. Spiking neural networks shows good performance of low power consumption when running on hardware. More and more researchers begin to use Spiking neural networks to study the performance of image recognition and other aspects. Although Spiking neural network has many limitations in accuracy and training difficulty, it has stimulated the research enthusiasm of many researchers. Spiking neural networks has developed rapidly, and many training methods can achieve the same or even higher accuracy than Artificial neural networks. In this paper, we further understand the advantages and framework of Spiking neural network through its development.