Statistical complexity and connectivity relationship in cultured neural networks

A. Tlaie, L.M. Ballesteros-Esteban, I. Leyva and I. Sendiña-Nadal

Chaos, Solitons & Fractals, 2019, vol. 119, issue C, 284-290

Abstract: We explore the interplay between the topological relevance of a neuron and its dynamical traces in experimental cultured neuronal networks. We monitor the growth and development of these networks to characterise the evolution of their connectivity. Then, we explore the structure-dynamics relationship by simulating a biophysically plausible dynamical model on top of each networks’ nodes. In the weakly coupling regime, the statistical complexity of each single node dynamics is found to be anti-correlated with their degree centrality, with nodes of higher degree displaying lower complexity levels. Our results imply that it is possible to infer the degree distribution of the network connectivity only from individual dynamical measurements.

Keywords: Complex networks; Neuron models; Network inference; Cultured networks (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:119:y:2019:i:c:p:284-290

DOI: 10.1016/j.chaos.2018.12.027

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