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神经系统中的合作:连接复杂性与周期性。

Cooperation in neural systems: bridging complexity and periodicity.

作者信息

Zare Marzieh, Grigolini Paolo

机构信息

Center for Nonlinear Science, University of North Texas, PO Box 311427, Denton, Texas 76203-1427, USA.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Nov;86(5 Pt 1):051918. doi: 10.1103/PhysRevE.86.051918. Epub 2012 Nov 29.

Abstract

Inverse power law distributions are generally interpreted as a manifestation of complexity, and waiting time distributions with power index μ<2 reflect the occurrence of ergodicity-breaking renewal events. In this paper we show how to combine these properties with the apparently foreign clocklike nature of biological processes. We use a two-dimensional regular network of leaky integrate-and-fire neurons, each of which is linked to its four nearest neighbors, to show that both complexity and periodicity are generated by locality breakdown: Links of increasing strength have the effect of turning local interactions into long-range interactions, thereby generating time complexity followed by time periodicity. Increasing the density of neuron firings reduces the influence of periodicity, thus creating a cooperation-induced renewal condition that is distinctly non-Poissonian.

摘要

幂律分布通常被解释为复杂性的一种表现,而幂指数μ<2的等待时间分布反映了遍历性破坏更新事件的发生。在本文中,我们展示了如何将这些特性与生物过程看似外来的时钟般性质相结合。我们使用一个二维规则的漏电积分发放神经元网络,其中每个神经元都与其四个最近邻相连,以表明复杂性和周期性都是由局部性破坏产生的:强度不断增加的连接具有将局部相互作用转变为长程相互作用的效果,从而产生时间复杂性,随后是时间周期性。增加神经元放电的密度会降低周期性的影响,从而创造出一种明显非泊松分布的合作诱导更新条件。

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