Quantitative and qualitative analysis of asynchronous neural activity

TL;DR

This paper investigates the asynchronous activity in sparse neural networks, revealing how strong coupling induces bursting behavior and long-tailed interspike intervals, with insights supported by self-consistent models and stochastic process analogies.

Contribution

It provides a detailed analysis of asynchronous neural dynamics, highlighting the effects of coupling strength and network quenched disorder on activity patterns, and introduces a self-consistent modeling approach.

Findings

Strong coupling leads to bursting activity.

Interspike interval distribution is long-tailed.

Quenched networks differ significantly from annealed ones.

Abstract

The activity of a sparse network of leaky integrate-and-fire neurons is carefully revisited with reference to a regime of a bona-fide asynchronous dynamics. The study is preceded by a finite-size scaling analysis, carried out to identify a setup where collective synchronization is negligible. The comparison between quenched and annealed networks reveals the emergence of substantial differences when the coupling strength is increased, via a scenario somehow reminiscent of a phase transition. For sufficiently strong synaptic coupling, quenched networks exhibit a highly bursting neural activity, well reproduced by a self-consistent approach, based on the assumption that the input synaptic current is the superposition of independent renewal processes. The distribution of interspike intervals turns out to be relatively long-tailed; a crucial feature required for the self-sustainment of the bursting activity in a regime where neurons operate on average (much) below threshold. A semi-quantitative analogy with Ornstein-Uhlenbeck processes helps validating this interpretation. Finally, an alternative explanation in terms of Poisson processes is offered under the additional assumption of mutual correlations among excitatory and inhibitory spikes.