Dynamical complexity in the C.elegans neural network

C. G. Antonopoulos, A. S. Fokas, T. C. Bountis

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4 Citations (Scopus)

Abstract

We model the neuronal circuit of the C.elegans soil worm in terms of a Hindmarsh-Rose system of ordinary differential equations, dividing its circuit into six communities which are determined via the Walktrap and Louvain methods. Using the numerical solution of these equations, we analyze important measures of dynamical complexity, namely synchronicity, the largest Lyapunov exponent, and the ΦAR auto-regressive integrated information theory measure. We show that ΦAR provides a useful measure of the information contained in the C.elegans brain dynamic network. Our analysis reveals that the C.elegans brain dynamic network generates more information than the sum of its constituent parts, and that attains higher levels of integrated information for couplings for which either all its communities are highly synchronized, or there is a mixed state of highly synchronized and desynchronized communities.

Original languageEnglish
Pages (from-to)1255-1269
Number of pages15
JournalEuropean Physical Journal: Special Topics
Volume225
Issue number6-7
DOIs
Publication statusPublished - Sep 1 2016
Externally publishedYes

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ASJC Scopus subject areas

  • Materials Science(all)
  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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