文章基本信息

标题：Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs
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作者：Christoph Fretter ; Annick Lesne ; Claus C. Hilgetag 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2017
卷号：7
期号：1
DOI：10.1038/srep42340
语种：English
出版社：Springer Nature
摘要：Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.