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神经元培养中功能连接的多尺度演化复杂网络模型。

Multiscale evolving complex network model of functional connectivity in neuronal cultures.

机构信息

Cybernetics Research Group, School of Systems Engineering, University of Reading, Reading, RG6 6AY, UK.

出版信息

IEEE Trans Biomed Eng. 2012 Jan;59(1):30-4. doi: 10.1109/TBME.2011.2171340. Epub 2011 Oct 13.

Abstract

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust, and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological self-organization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modeling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

摘要

在多电极阵列上培养的皮质神经元培养物表现出自发的、强大的、反复出现的高度同步活动模式,称为爆发。这些爆发在神经元网络的发育和拓扑自组织中起着至关重要的作用。因此,了解这些爆发中同步性的演变可以深入了解网络的增长以及学习和记忆中涉及的功能过程。可以通过观察爆发期间演变的同步模式来构建功能连接网络。为了捕捉这种演变,采用了一种使用新兴的复杂网络演化框架的建模方法,并通过利用系统的多个时间尺度,旨在展示顺序和有序同步在网络功能中的重要性。

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