Emergence of assortative mixing between clusters of cultured neurons

Abstract

The analysis of the activity of neuronal cultures is considered to be a good proxy of the functional connectivity of in vivo neuronal tissues. Thus, the functional complex network inferred from activity patterns is a promising way to unravel the interplay between structure and functionality of neuronal systems. Here, we monitor the spontaneous self-sustained dynamics in neuronal cultures formed by interconnected aggregates of neurons (clusters). Dynamics is characterized by the fast activation of groups of clusters in sequences termed bursts. The analysis of the time delays between clusters' activations within the bursts allows the reconstruction of the directed functional connectivity of the network. We propose a method to statistically infer this connectivity and analyze the resulting properties of the associated complex networks. Surprisingly enough, in contrast to what has been reported for many biological networks, the clustered neuronal cultures present assortative mixing connectivity values, as well as a rich--club core, meaning that there is a preference for clusters to link to other clusters that share similar functional connectivity, which shapes a `connectivity backbone' in the network. These results point out that the grouping of neurons and the assortative connectivity between clusters are intrinsic survival mechanisms of the culture.

33 pages, 10 figures

References in corpus (6)

• Scale-free brain functional networks
• The functional structure of cortical neuronal networks grown in vitro
• The Physics of Living Neural Networks
• On the rich-club effect in dense and weighted networks
• Emergence of small-world anatomical networks in self-organizing clustered neuronal cultures
• Rich-club vs rich-multipolarization phenomena in weighted networks

Cited by in corpus (8)

• A topological criterion for filtering information in complex brain networks
• Degree correlations in directed scale-free networks
• Modular architecture facilitates noise-driven control of synchrony in neuronal networks
• Benchmarking measures of network controllability on canonical graph models
• Modeling the Network Dynamics of Pulse-Coupled Neurons
• Assortativity and leadership emergence from anti-preferential attachment in heterogeneous networks
• On dynamic random graphs with degree homogenization via anti-preferential attachment probabilities
• Networks with degree-degree correlations is a special case of edge-coloured random graphs