Dynamic Neural Mechanisms for Recognizing Spike Trains

摘要：

Dynamic neural networks are designed to discuss how the dynamic mechanisms in the neurons and synapses work in recognizing interspike intervals (ISIs). The threshold integration of post-synaptic membrane potentials, the refractory period of neurons, together with the spike-time-dependent plasticity (STDP) learning rule are discussed. Based on these dynamic mechanisms, the input inter-spike interval sequences are decomposed into isolated spikes. The synoptic delay times modulated by STDP learning rule is the key mechanism in the ISIs recognition, based on which the ISIs are learned and saved in the delay times. After learning, the neural networks could recognize whether different input sequences include the same consecutive ISIs.

作者: Yan Liu Liujun Chen Jiawei Chen Qinghua Chen Fukang Fang

作者单位: Department of Systems Science School of Management Beijing Normal University Beijing 100875, P.R.Chi State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing 100875

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 584-587

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）