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Neural complexity and structural connectivity

Record type: Article

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.

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Citation

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).

More information

Published date: 19 May 2009
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 267384
URI: http://eprints.soton.ac.uk/id/eprint/267384
ISSN: 1539-3755
PURE UUID: f3355364-a632-48fc-8b66-56d5fa6324fd

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Date deposited: 20 May 2009 15:30
Last modified: 18 Jul 2017 07:05

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Contributors

Author: Lionel Barnett

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