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随机分层系统:可激发动力学

Stochastic hierarchical systems: excitable dynamics.

作者信息

Leonhardt Helmar, Zaks Michael A, Falcke Martin, Schimansky-Geier Lutz

机构信息

Institute of Physics, Humboldt University at Berlin, Newtonstr. 15, D-12489, Berlin, Germany,

出版信息

J Biol Phys. 2008 Oct;34(5):521-38. doi: 10.1007/s10867-008-9112-1. Epub 2008 Oct 1.

Abstract

We present a discrete model of stochastic excitability by a low-dimensional set of delayed integral equations governing the probability in the rest state, the excited state, and the refractory state. The process is a random walk with discrete states and nonexponential waiting time distributions, which lead to the incorporation of memory kernels in the integral equations. We extend the equations of a single unit to the system of equations for an ensemble of globally coupled oscillators, derive the mean field equations, and investigate bifurcations of steady states. Conditions of destabilization are found, which imply oscillations of the mean fields in the stochastic ensemble. The relation between the mean field equations and the paradigmatic Kuramoto model is shown.

摘要

我们通过一组低维延迟积分方程提出了一种随机兴奋性的离散模型,该方程控制着静息态、激发态和不应态的概率。该过程是具有离散状态和非指数等待时间分布的随机游走,这导致在积分方程中纳入记忆核。我们将单个单元的方程扩展到全局耦合振子集合的方程组,推导平均场方程,并研究稳态的分岔。发现了失稳条件,这意味着随机集合中平均场的振荡。展示了平均场方程与典型的Kuramoto模型之间的关系。

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