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[受刺激的大规模神经元群体的随机非线性演化模型与动态神经编码]

[Stochastic nonlinear evolutional model of the large-scaled neuronal population and dynamic neural coding subject to stimulation].

作者信息

Wang Rubin, Yu Wei

机构信息

Institute for Brain Information Processing and Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2006 Apr;23(2):243-7.

Abstract

In this paper, we investigate how the population of neuronal oscillators deals with information, and analyze the dynamic evolution of neural coding when the outer stimulation acts on it on the base of our former work. By numerically computing for the model, we obtain the figure of average number density, which is used to describe the action potential of the neurons within population in three-dimensional space, namely the dynamic evolution of neural coding. The result of numerical analysis indicates that the model in this paper can be used to describe the evolutional process of abundant mutual interactional neurons acted by outer stimulation. The numerical result also proves that only the suitable stimulation can change the coupling structure of neurons. And the evolution model given in this paper incarnates the neural plasticity.

摘要

在本文中,我们在前作的基础上,研究神经元振荡器群体如何处理信息,并分析当外部刺激作用于其上时神经编码的动态演化。通过对该模型进行数值计算,我们得到了平均数密度图,其用于描述三维空间中群体内神经元的动作电位,即神经编码的动态演化。数值分析结果表明,本文中的模型可用于描述受外部刺激作用的大量相互作用神经元的演化过程。数值结果还证明,只有合适的刺激才能改变神经元的耦合结构。并且本文给出的演化模型体现了神经可塑性。

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