Neural complexity and structural connectivity

Lionel Barnett, Christopher L. Buckley, Seth Bullock

Research output: Contribution to journalArticle (Academic Journal)peer-review

43 Citations (Scopus)

Abstract

Tononi et al. Proc. Natl. Acad. Sci. U.S.A. 91, 5033 1994 proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond.We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model.
Original languageEnglish
Pages (from-to)051914-[12pp]
JournalPhysical Review E: Statistical, Nonlinear, and Soft Matter Physics
Volume79
Issue number5
Early online date19 May 2009
DOIs
Publication statusPublished - 2009

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