期刊
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
卷 -, 期 -, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1742-5468/2013/03/P03012
关键词
neuronal networks (theory); random graphs; networks; computational neuroscience
资金
- NSF [DMS-0817649, DMS-1122094, DMS-1056125, DMS-0818153]
- Texas ARP/ATP award
- Career Award at the Scientific Interface from the Burroughs Wellcome Fund
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1122094, 1122106] Funding Source: National Science Foundation
Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state.
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