Decomposition of Superimposed Chaotic Spike Sequences by using The Bifurcating Neuron
Decomposition of Superimposed Chaotic Spike Sequences by using The Bifurcating Neuron
复制标题
使用分叉神经元分解叠加混沌尖峰序列
DOI：
10.1007/978-981-10-8854-4_1
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Masao Kubo
中科院分区：
文献类型：
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作者：
Akihiro Yamaguchi;Yutaka Yamaguti;Masao Kubo
本文从神经信息编码的角度研究了叠加混沌尖峰序列的分解问题。我们构造了简单的分叉神经元网络，并引入耦合模型来分解叠加的混沌棘波序列。通过数值模拟验证了该方法的分解性能，并用同步尖峰比率对其进行了评价。结果表明，对于叠加的两个混沌尖峰序列，大约90%的尖峰被正确分解。
In this study, decomposition of superimposed chaotic spike sequence was investigated from the view point of neural information coding. We construct simple network of bifurcating neuron and introduce the coupling model to decompose superimposed chaotic spike sequences. The decomposing performance was demonstrated by the numerical simulation and evaluated by the ratio of synchronized spikes. As a result, for the superimposed two chaotic spike sequences, approximately 90% of spikes were correctly decomposed.