Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs
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Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs. / Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C; Hütt, Marc-Thorsten.
in: SCI REP-UK, Jahrgang 7, 10.02.2017, S. 42340.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs
AU - Fretter, Christoph
AU - Lesne, Annick
AU - Hilgetag, Claus C
AU - Hütt, Marc-Thorsten
PY - 2017/2/10
Y1 - 2017/2/10
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.1038/srep42340
DO - 10.1038/srep42340
M3 - SCORING: Journal article
C2 - 28186182
VL - 7
SP - 42340
JO - SCI REP-UK
JF - SCI REP-UK
SN - 2045-2322
ER -