Neural complexity and structural connectivity

Barnett, Lionel and Buckley, Christopher L. (2009) Neural complexity and structural connectivity Physical Review E, 79, (5), 051914-[12pp]. (doi:10.1103/PhysRevE.79.051914).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1103/PhysRevE.79.051914
ISSNs: 1539-3755 (print)
Organisations: Agents, Interactions & Complexity
ePrint ID: 267384
Date :
Date Event
19 May 2009Published
Date Deposited: 20 May 2009 15:30
Last Modified: 17 Apr 2017 18:49
Further Information:Google Scholar

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