Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks.
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.
School of Engineering and Science, Jacobs University Bremen Bremen, Germany.
Guadalupe C
GC
Garcia
Annick
A
Lesne
Marc-Thorsten
MT
Hütt
Claus C
CC
Hilgetag