Izhikevich神经元模型峰值数据的非线性时间序列分析

2020 International SoC Design Conference (ISOCC) Pub Date : 2020-10-21 DOI:10.1109/ISOCC50952.2020.9333002

Y. Uwate, Y. Nishio, M. Obien, U. Frey

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引用次数: 0

摘要

众所周知，神经网络的突发模式可能在大脑的信息处理中起着重要作用。我们认为使用产生突发模式的数学神经元模型构建模型是有利的，因为与真实的生物神经元数据相比，这样的模型更容易研究和更容易获取。在这项研究中，我们使用Izhikevich神经元模型来产生突发模式，并对Izhikevich神经元数据应用递归图密度熵。

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Nonlinear Time Series Analysis of Spike Data of Izhikevich Neuron Model

It is well known that burst patterns of neuronal networks may play an important role in information processing in the brain. We consider that it is advantageous to construct a model using mathematical neuronal models producing burst patterns, because it is such models are easier to study and more accessible as compared to real biological neuronal data. In this study, we use the Izhikevich neuron model to produce burst patterns and apply a recurrence plot density entropy to the Izhikevich neuron data.

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2020 International SoC Design Conference (ISOCC)

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