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Dynamical complexity in the C.elegans neural network

Antonopoulos, CG and Fokas, AS and Bountis, TC (2016) 'Dynamical complexity in the C.elegans neural network.' European Physical Journal: Special Topics, 225 (6-7). 1255 - 1269. ISSN 1951-6355

paper_v11_AFB_4_EPJST.pdf - Accepted Version

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Chris Antonopoulos
Date Deposited: 08 Sep 2016 13:12
Last Modified: 22 Jun 2021 17:15

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