Statistical complexity and connectivity relationship in cultured neural networks

TL;DR

This study investigates how the topological importance of neurons in cultured networks relates to their dynamical complexity, revealing that highly connected neurons tend to have simpler dynamics, enabling network connectivity inference from individual neuron data.

Contribution

It demonstrates a correlation between neuronal connectivity and dynamical complexity, providing a method to infer network structure from single-node measurements.

Findings

Higher degree neurons exhibit lower dynamical complexity.

Statistical complexity is anti-correlated with node degree.

Network connectivity can be inferred from individual neuron dynamics.

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.