Improving multi-layer spiking neural networks by incorporating brain-inspired rules

作     者：Yi ZENG Tielin ZHANG Bo XU 

作者机构：Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of Sciences 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2017年第60卷第5期

页      面：226-236页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB02060007) Beijing Municipal Commission of Science and Technology (Grant Nos. Z151100000915070, Z161100000216124) 

主　　题：brain-inspired rules spiking neural network plasticity classification task 

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 ***, 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.