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Dynamical complexity in the C.elegans neural network

Antonopoulos, CG and Fokas, A and Bountis, T (2016) 'Dynamical complexity in the C.elegans neural network.' European Physical Journal Special Topics, 225 (6-7). pp. 1255-1269. ISSN 1951-6355

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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 equa- tions, 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 com- plexity, namely synchronicity, the largest Lyapunov exponent, and the ?AR auto-regressive integrated information theory measure. We show that ?AR 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 de- synchronized communities.

Item Type: Article
Additional Information: 20 pages, 2 figures
Uncontrolled Keywords: q-bio.NC; nlin.CD
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Sep 2016 13:12
Last Modified: 15 Jan 2022 00:19

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