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.

翻译：在下一代类脑人工智能快速演进且电磁环境日益复杂的背景下，脉冲神经网络因其高度仿生特性与抗干扰能力，在计算速度、实时信息处理及时空信息处理方面展现出巨大潜力。脉冲神经网络是类脑人工智能的核心之一，通过模拟生物神经网络的结构与信息传递模式实现类脑计算。本文总结了五种神经元模型的优缺点及适用性，并分析了五种网络拓扑结构的特点；随后综述了脉冲神经网络算法，从无监督学习与有监督学习两个角度，概括了基于突触可塑性规则的无监督学习算法及四类有监督学习算法；最后重点评述了国内外正在研发的类脑神经形态芯片。本文旨在通过系统性总结，为初涉脉冲神经网络研究领域的研究者提供学习思路与研究方向。