Learning rules in spiking neural networks: A survey

Zexiang Yi, Jing Lian, Qidong Liu, Hegui Zhu, Dong Liang, Jizhao Liu

Abstract: Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal patterns, and low-power consumption. The complex intrinsic properties of SNNs give rise to a diversity of their learning rules which are essential to functional SNNs. This paper is aimed at presenting a comprehensive overview of learning rules in SNNs. Firstly, we introduce the basic concepts of SNNs and commonly used neuromorphic datasets. Then, guided by a hierarchical classification of SNN learning rules, we present a comprehensive survey of these rules with discussions on their characteristics, advantages, limitations, and performance on several datasets. Moreover, we review practical applications of SNNs, including event-based vision and audio signal processing. Finally, we conclude this survey with a discussion on challenges and promising future research directions in this area.