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Improving multi-layer spiking neural networks by incorporating brain-inspired rules
Zeng, Yi1,2; Zhang, Tielin1; Xu, Bo1,2
2017-05-01
发表期刊SCIENCE CHINA-INFORMATION SCIENCES
卷号60期号:5页码:052201:01-12
文章类型Article
摘要This paper introduces seven brain-inspired rules that are deeply rooted in the understanding of the brain to improve multi-layer spiking neural networks (SNNs). The dynamics of neurons, synapses, and plasticity models are considered to be major characteristics of information processing in brain neural networks. Hence, incorporating these models and rules to traditional SNNs is expected to improve their efficiency. The proposed SNN model can mainly be divided into three parts: the spike generation layer, the hidden layers, and the output layer. In the spike generation layer, non-temporary signals such as static images are converted into spikes by both local and global feature-converting methods. In the hidden layers, the rules of dynamic neurons, synapses, the proportion of different kinds of neurons, and various spike timing dependent plasticity (STDP) models are incorporated. In the output layer, the function of classification for excitatory neurons and winner take all (WTA) for inhibitory neurons are realized. MNIST dataset is used to validate the classification accuracy of the proposed neural network model. Experimental results show that higher accuracy will be achieved when more brain-inspired rules (with careful selection) are integrated into the learning procedure.
关键词Brain-inspired Rules Spiking Neural Network Plasticity Classification Task
WOS标题词Science & Technology ; Technology
DOI10.1007/s11432-016-0439-4
关键词[WOS]SYNAPTIC PLASTICITY ; NEURONAL-ACTIVITY ; MODEL ; ALGORITHM ; STDP
收录类别SCI
语种英语
项目资助者Strategic Priority Research Program of Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z151100000915070 ; Z161100000216124)
WOS研究方向Computer Science
WOS类目Computer Science, Information Systems
WOS记录号WOS:000405775100001
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15272
专题类脑智能研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
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Zeng, Yi,Zhang, Tielin,Xu, Bo. Improving multi-layer spiking neural networks by incorporating brain-inspired rules[J]. SCIENCE CHINA-INFORMATION SCIENCES,2017,60(5):052201:01-12.
APA Zeng, Yi,Zhang, Tielin,&Xu, Bo.(2017).Improving multi-layer spiking neural networks by incorporating brain-inspired rules.SCIENCE CHINA-INFORMATION SCIENCES,60(5),052201:01-12.
MLA Zeng, Yi,et al."Improving multi-layer spiking neural networks by incorporating brain-inspired rules".SCIENCE CHINA-INFORMATION SCIENCES 60.5(2017):052201:01-12.
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