基于忆阻器的脉冲神经网络研究综述  

Review of the research of spiking neuron network based on memristor

作　　者：徐桂芝 姚林静 李子康 XU Guizhi;YAO Linjing;LI Zikang(Department of Electrical Engineering, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China)

机构地区：[1]河北工业大学电气工程学院电气工程系,天津300130

出　　处：《生物医学工程学杂志》2018年第3期475-480,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金面上项目(61571180)

摘　　要：人工智能的快速发展对计算神经科学的计算速度、资源消耗和生物解释性提出了更高的要求。脉冲神经网络能够携带大量信息,实现对大脑信息处理方式的模仿。它的硬件化是实现其强大计算能力的重要途径,但也是极具挑战性的技术难题。忆阻器是目前功能最接近神经元突触的电子器件,能够以与生物大脑高度相似的脉冲时间依赖可塑性(STDP)机制响应脉冲电压,成为近几年研究构建脉冲神经网络硬件电路的热点。本文通过查阅国内外相关文献,对近几年基于忆阻器的脉冲神经网络的研究工作进行了深入了解和介绍。The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of information, and realize the imitation of brain information processing. However, its hardware is an important way to realize its powerful computing ability, and it is also a challenging technical problem. The memristor currently is the electronic devices that functions closest to the neuron synapse, and able to respond to spike voltage in a highly similar spike timing dependent plasticity（STDP） mechanism with a biological brain, and has become a research hotspot to construct spiking neuron networks hardware circuit in recent years. Through consulting the relevant literature at home and abroad, this paper has made a thorough understanding and introduction to the research work of the spiking neuron networks based on the memristor in recent years.

关 键 词：人工智能 脉冲神经网络 忆阻器 脉冲时间依赖可塑性机制 

分 类 号：TN60[电子电信—电路与系统] TP18[自动化与计算机技术—控制理论与控制工程]