引用本文:胡一凡,李国齐,吴郁杰,等.脉冲神经网络研究进展综述[J].控制与决策,2021,36(1):1-26
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脉冲神经网络研究进展综述
胡一凡1,李国齐1, 吴郁杰1, 邓磊2
(1. 清华大学类脑计算研究中心,北京100084;2. 加州大学圣塔芭芭拉分校电子与计算机工程系,美国CA 93106)
摘要:
近年来,起源于计算神经科学的脉冲神经网络因其具有丰富的时空动力学特征、多样的编码机制、契合硬件的事件驱动特性等优势,在神经形态工程和类脑计算领域已得到广泛的关注.脉冲神经网络与当前计算机科学导向的以深度卷积网络为代表的人工神经网络的交叉融合被认为是发展人工通用智能的有力途径.对此,回顾了脉冲神经网络的发展历程,将其划分为神经元模型、训练算法、编程框架、数据集以及硬件芯片等5个重点方向,全方位介绍脉冲神经网络的最新进展和内涵,讨论并分析了脉冲神经网络领域各个重点方向的发展机遇和挑战.希望本综述能够吸引不同学科的研究者,通过跨学科的思想交流与合作研究,推动脉冲神经网络领域的发展.
关键词:  脉冲神经网络  神经网络  类脑计算  神经形态计算  深度学习  人工通用智能
DOI:10.13195/j.kzyjc.2020.1006
分类号:TP301.6
基金项目:科技部重点研发项目(2018YEF0200200,2018AAA0102600);国家自然科学基金项目(61876215, 61836004);清华大学国强研究院支持项目;广州市重点研发计划项目(20200703006).
Spiking neural networks:A survey on recent advances and new directions
HU Yi-fan1,LI Guo-qi1,WU Yu-jie1,DENG Lei2
(1. Center for Brain-Inspired Computing Research,Tsinghua University, Beijing100084,China;2. Department of Electrical and Computer Engineering,University of California,Santa Barbara,CA93106, USA)
Abstract:
Over the past few years, spiking neural networks(SNNs) originated from computational neuroscience have received extensive attention in the field of neuromorphic engineering and brain inspired computing due to their rich spatio-temporal dynamics, diverse coding schemes, and event-driven characteristics that naturally fit the neuromorphic hardware. Combining neuroscience-oriented spiking neural networks with computer-science-oriented artificial neural networks such as deep convolutional neural networks, is considered as a promising approach to artificial general intelligence. In this survey, we review the recent advances of spiking neural networks and focus on five major research areas, which we define as spiking neuron models, SNN algorithms, programming frameworks, datasets and neuromorphic hardware, and conclude with a broad discussion on the opportunities and challenges. The goals of this work are to provide an exhaustive review of spiking neural networks, attract researchers in different disciplines and motivate new works in this field through interdisciplinary exchange of ideas and collaborative research.
Key words:  spiking neural networks  neural networks  brain-inspired computing  neuromorphic computing  deep learning  artificial general intelligence

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