Dynamical Complexity in the C.elegans Neural Network
Chris G. Antonopoulos, Athanasios S. Fokas, Tassos C. Bountis
arXiv Preprint Archive October 25, 2015 via arXiv
Study at a glance
AI-extracted from the abstract| Characteristics | Computational modeling study Peer reviewed |
|---|---|
| Keywords | Q-bio.nc Nlin.cd |
| Key points | The C. elegans brain dynamic network generates more integrated information than the sum of its parts, with highest levels occurring under conditions of high synchronization or a mix of synchronized and desynchronized communities, associated with low chaos. |
Abstract
We model the neuronal circuit of the C.elegans soil worm in terms of Hindmarsh-Rose systems of ordinary differential equations, dividing its circuit into six communities pointed out by 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 $Φ_{\mbox{AR}}$ auto-regressive integrated information theory measure, which has been suggested to reflect different levels of consciousness. We show that $Φ_{\mbox{AR}}$ 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 desynchronized communities. Both situations are characterized by relatively low chaotic behavior.