刺激作用下大规模神经元群的随机非线性演化模型及动态神经编码

在前期研究工作的基础上，我们考虑外刺激作用下神经振子群对信息的处理及神经编码的动态演化。通过对模型的数值分析，得到了在三维空间用于描述神经元群内神经元放电过程时的数密度随时间演化的图像，即神经编码的动态演化。数值分析的结果表明该模型能够用来描述大量相互作用神经元在刺激下神经编码的演化过程，研究证明只有在适当的刺激强度下神经元之间的耦合强度与耦合结构才能发生改变，从而体现了神经元的可塑性变化。

Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

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.