Zhang Huigang, Xu Guizhi, Guo Jiarong, Guo Lei
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China.
Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Oct 25;38(5):986-994. doi: 10.7507/1001-5515.202011005.
Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.
在类脑人工智能快速发展和电磁环境日益复杂的当前形势下，最具仿生特性和抗干扰能力的脉冲神经网络在计算速度、实时信息处理以及时空数据处理方面展现出了巨大潜力。脉冲神经网络是类脑人工智能的核心部分，它通过模拟生物神经网络的结构和信息传输方式来实现类脑计算。本文首先总结了五种模型的优缺点，并分析了几种网络拓扑结构的特点。然后，总结了脉冲神经网络算法。分析了基于脉冲时间依赖可塑性（STDP）规则的无监督学习和四种类型的监督学习算法。最后，综述了国内外类脑神经形态芯片的研究情况。本文旨在为脉冲神经网络领域的新同行提供学习思路和研究方向。
