A Review of Spiking Neural Networks

School of Communication Engineering, Jilin University- Lambton College, Changchun, Jilin Province, China, 130012

^* Corresponding author. Email: 1151890419@qq.com

Abstract

Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural models, coding methods, learning algorithms, and neuromorphic computing platforms, and according to these to propose some expected development, such as improving the balance between bio-mimicry and cost of computing for neuron models, compounding coding methods, unsupervised learning algorithms in SNN, and digital-analog computing platform.