Supervised learning in spiking neural networks: A review of algorithms and evaluations

尖峰神经网络 计算机科学 人工神经网络 人工智能 机器学习 监督学习 人工神经网络的类型 随机神经网络 领域(数学) 无监督学习 深度学习 循环神经网络 算法 数学 纯数学
作者
Xiangwen Wang,Xianghong Lin,Xiaochao Dang
出处
期刊:Neural Networks [Elsevier BV]
卷期号:125: 258-280 被引量:165
标识
DOI:10.1016/j.neunet.2020.02.011
摘要

As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural networks are composed of biologically plausible spiking neurons, which have become suitable tools for processing complex temporal or spatiotemporal information. However, because of their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, and has become an important problem in this research field. This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively. First, a comparison between spiking neural networks and traditional artificial neural networks is provided. The general framework and some related theories of supervised learning for spiking neural networks are then introduced. Furthermore, the state-of-the-art supervised learning algorithms in recent years are reviewed from the perspectives of applicability to spiking neural network architecture and the inherent mechanisms of supervised learning algorithms. A performance comparison of spike train learning of some representative algorithms is also made. In addition, we provide five qualitative performance evaluation criteria for supervised learning algorithms for spiking neural networks and further present a new taxonomy for supervised learning algorithms depending on these five performance evaluation criteria. Finally, some future research directions in this research field are outlined.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
骤世界完成签到 ,获得积分10
1秒前
Owen应助Yamin采纳,获得10
1秒前
刘武函完成签到,获得积分10
1秒前
吐司发布了新的文献求助10
1秒前
YAN完成签到,获得积分10
1秒前
1秒前
Michael_li完成签到,获得积分10
2秒前
CipherSage应助zzk采纳,获得10
3秒前
香香发布了新的文献求助10
3秒前
yydragen完成签到,获得积分0
3秒前
song发布了新的文献求助10
4秒前
康小姐完成签到,获得积分10
4秒前
xxn发布了新的文献求助10
5秒前
6秒前
茶烟梧月发布了新的文献求助30
6秒前
扶光完成签到 ,获得积分10
6秒前
露露完成签到,获得积分10
6秒前
7秒前
7秒前
mmol完成签到,获得积分10
7秒前
我爱陶子完成签到 ,获得积分10
7秒前
帆帆帆发布了新的文献求助10
7秒前
ZDY完成签到,获得积分10
8秒前
标致醉波完成签到,获得积分10
8秒前
8秒前
111完成签到,获得积分10
8秒前
9秒前
bkagyin应助yunsww采纳,获得10
9秒前
小蜜蜂完成签到,获得积分10
9秒前
9秒前
li发布了新的文献求助10
10秒前
茗姜发布了新的文献求助60
10秒前
朴实的草丛完成签到 ,获得积分10
10秒前
张洁纯发布了新的文献求助30
10秒前
10秒前
徐一一发布了新的文献求助10
10秒前
bkagyin应助淡定的鸭子采纳,获得10
10秒前
牟弼完成签到,获得积分10
10秒前
11秒前
11秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969322
求助须知:如何正确求助?哪些是违规求助? 3514152
关于积分的说明 11172188
捐赠科研通 3249407
什么是DOI,文献DOI怎么找? 1794832
邀请新用户注册赠送积分活动 875437
科研通“疑难数据库(出版商)”最低求助积分说明 804781