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Improving multi-layer spiking neural networks by incorporating brain-inspired rules
Zeng, Yi1,2; Zhang, Tielin1; Xu, Bo1,2
Source PublicationSCIENCE CHINA-INFORMATION SCIENCES
2017-05-01
Volume60Issue:5Pages:052201:01-12
SubtypeArticle
AbstractThis 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.
KeywordBrain-inspired Rules Spiking Neural Network Plasticity Classification Task
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s11432-016-0439-4
WOS KeywordSYNAPTIC PLASTICITY ; NEURONAL-ACTIVITY ; MODEL ; ALGORITHM ; STDP
Indexed BySCI
Language英语
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z151100000915070 ; Z161100000216124)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000405775100001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15272
Collection类脑智能研究中心
Affiliation1.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
Recommended Citation
GB/T 7714
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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