Improving multi-layer spiking neural networks by incorporating brain-inspired rules
文献类型：期刊论文
| 作者 | Zeng, Yi1,2 ; Zhang, Tielin1; Xu, Bo1,2
|
| 刊名 | SCIENCE CHINA-INFORMATION SCIENCES
 |
| 出版日期 | 2017-05-01 |
| 卷号 | 60期号:5页码:052201:01-12 |
| 关键词 | Brain-inspired Rules Spiking Neural Network Plasticity Classification Task |
| DOI | 10.1007/s11432-016-0439-4 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | SYNAPTIC PLASTICITY ; NEURONAL-ACTIVITY ; MODEL ; ALGORITHM ; STDP |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000405775100001 |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z151100000915070 ; Z161100000216124) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15272]  |
| 专题 | 类脑智能研究中心_类脑认知计算 |
| 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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