building blocks of self-sustained activity in a simple deterministic model of excitable neural networks
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2012
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Abstract
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.
| Reference Key |
garcia2012frontiersbuilding
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|---|---|
| Authors | ;Guadalupe Clara Garcia;Annick eLesne;Annick eLesne;Marc-Thorsten eHütt;Claus C. Hilgetag;Claus C. Hilgetag;Claus C. Hilgetag |
| Journal | population health management |
| Year | 2012 |
| DOI |
10.3389/fncom.2012.00050
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| URL | |
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