  In the rapid evolution of next-generation brain-inspired artificial
intelligence and increasingly sophisticated electromagnetic environment, the
most bionic characteristics and anti-interference performance of spiking neural
networks show great potential in terms of computational speed, real-time
information processing, and spatio-temporal information processing. Data
processing. Spiking neural network is one of the cores of brain-like artificial
intelligence, which realizes brain-like computing by simulating the structure
and information transfer mode of biological neural networks. This paper
summarizes the strengths, weaknesses and applicability of five neuronal models
and analyzes the characteristics of five network topologies; then reviews the
spiking neural network algorithms and summarizes the unsupervised learning
algorithms based on synaptic plasticity rules and four types of supervised
learning algorithms from the perspectives of unsupervised learning and
supervised learning; finally focuses on the review of brain-like neuromorphic
chips under research at home and abroad. This paper is intended to provide
learning concepts and research orientations for the peers who are new to the
research field of spiking neural networks through systematic summaries.
