关于注意和记忆的神经动力学机制

利用随机的相变动力学理论研究了一个具有不同相位的神经振子群模型，并考察神经 振子群对刺激信息的处理及神经编码的动态演化. 通过对动力学模型的数值分析，在二维相 空间上描述了神经元集群内不同振子簇发放动作电位时，数密度随时间演化的图像. 数值分 析的结果表明该模型能够用来描述注意和记忆的神经动力学机制，并且证明了只有高维的神 经动力学模型才能更深刻地描述神经元集群的动力学特性，而以往的编码模型丢失了大量有 用的神经信息.

MECHANISM OF NEURODYNAMICS ON ATTENTION AND MEMORY

In this paper we propose a new nonlinear stochastic dynamic evolution model for phase encoding in population of neuronal oscillators with different phases, and study the neural information processing in cerebral cortex and dynamic evolution under the action of different stimulation signals. The evolution of the averaged number density along with time in the space of three dimensions is described in different clusters of neuronal oscillator firing action potential at different phase spaces by means of numerical analysis. The results of numerical analysis show that the dynamic model proposed in this paper can be used to describe the mechanism of neurodynamics of attention and memory, and it is proved that only the neural dynamic model in a high-dimension space can adequately describe dynamic characteristics of the neural population, and much useful neural information may be lost in the early models of stochastic dynamics for phase coding.