SUPERVISED AND AGGREGATE-LABEL LEARNING ALGORITHM OF SPIKING NEURAL NETWORK

被引：0

作者：

Chang, Haoyang ^{[1
]}

Li, Jianping ^{[1
]}

Feng, Mingchao ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Peoples R China

[2] JD AI Res, Chengdu, Peoples R China

来源：

2019 16TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICWAMTIP) | 2019年

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

Spiking neurons; Spiking neural networks; Supervised learning; Aggregate-label learning; Synaptic weight;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Spiking Neural Networks (SNNs) is closer to biological neural working mechanisms. Compared with the traditional neural network using rate coding, SNNs are able to process and abstract features from the temporal dynamics encoded in spike signals, thus prompting SNNs more biologically plausible and easier to implement on hardware. This paper describes the existing supervised and aggregate-label classic learning algorithms of SNNs, analyzes the merits and demerits of these algorithms, discusses the development direction of SNNs, and provides a basis for the research of efficient learning algorithms.

引用

页码：33 / 36

页数：4

共 21 条

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