Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth.

Samora Okujeni,Ulrich Egert

doi:10.7554/elife.47996

Abstract

The spatial distribution of neurons and activity-dependent neurite outgrowth shape long-range interaction, recurrent local connectivity and the modularity in neuronal networks. We investigated how this mesoscale architecture develops by interaction of neurite outgrowth, cell migration and activity in cultured networks of rat cortical neurons and show that simple rules can explain variations of network modularity. In contrast to theoretical studies on activity-dependent outgrowth but consistent with predictions for modular networks, spontaneous activity and the rate of synchronized bursts increased with clustering, whereas peak firing rates in bursts increased in highly interconnected homogeneous networks. As Ca2+ influx increased exponentially with increasing network recruitment during bursts, its modulation was highly correlated to peak firing rates. During network maturation, long-term estimates of Ca2+ influx showed convergence, even for highly different mesoscale architectures, neurite extent, connectivity, modularity and average activity levels, indicating homeostatic regulation towards a common set-point of Ca2+ influx.

Highlights

Modularity is a fundamental design principle of neuronal systems and exists at the scale of cellular compartments, local circuits or interconnected brain areas
This distribution is dynamic because neurons migrate even in postnatal development
The input a neuron receives depends on its embedding as well as the network’s overall connectivity and activity structure

Summary

Introduction

Modularity is a fundamental design principle of neuronal systems and exists at the scale of cellular compartments, local circuits or interconnected brain areas. At the mesoscale level of local circuits, the cerebral cortex is organized in local clusters of tightly interconnected neurons (Feldman and Peters, 1974; Skoglund et al, 2004) that share common inputs and targets (Bosking et al, 1997; Voges et al, 2010), have similar functional properties (Ringach et al, 2016) and are thought to constitute a basic computational module (Buxhoeveden and Casanova, 2002; Casanova and Casanova, 2019; Mountcastle, 1997). Computational models predict that modular connectivity, in turn, promotes spontaneous activity (Kaiser and Hilgetag, 2010; Klinshov et al, 2014; Mazzucato et al, 2015). Modularization and spontaneous activity may co-evolve in a self-enhancing process

Results

Discussion

Conclusion