Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks.
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Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks. / Garcia, Guadalupe C; Lesne, Annick; Hütt, Marc-Thorsten; Hilgetag, Claus.
in: FRONT COMPUT NEUROSC, Jahrgang 6, 2012, S. 50.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks.
AU - Garcia, Guadalupe C
AU - Lesne, Annick
AU - Hütt, Marc-Thorsten
AU - Hilgetag, Claus
PY - 2012
Y1 - 2012
N2 - Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic excitable model to explore how network-wide activation patterns are shaped by network architecture. Our observables are co-activation patterns, together with the average activity of the network and the periodicities in the excitation density. Our main results are: (1) the dependence of the correlation between the adjacency matrix and the instantaneous (zero time delay) co-activation matrix on global network features (clustering, modularity, scale-free degree distribution), (2) a correlation between the average activity and the amount of small cycles in the graph, and (3) a microscopic understanding of the contributions by 3-node and 4-node cycles to sustained activity.
AB - Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic excitable model to explore how network-wide activation patterns are shaped by network architecture. Our observables are co-activation patterns, together with the average activity of the network and the periodicities in the excitation density. Our main results are: (1) the dependence of the correlation between the adjacency matrix and the instantaneous (zero time delay) co-activation matrix on global network features (clustering, modularity, scale-free degree distribution), (2) a correlation between the average activity and the amount of small cycles in the graph, and (3) a microscopic understanding of the contributions by 3-node and 4-node cycles to sustained activity.
M3 - SCORING: Journal article
VL - 6
SP - 50
JO - FRONT COMPUT NEUROSC
JF - FRONT COMPUT NEUROSC
SN - 1662-5188
ER -
