Desynchronized stable states in diluted neural networks

TL;DR

This paper explores the dynamical phases of diluted inhibitory neural networks, revealing stable desynchronized states, transient irregular activity, and partial synchronization under external stimuli, with detailed analysis of inter-spike interval distributions.

Contribution

It introduces a detailed characterization of phase transitions in inhibitory neural networks, highlighting a novel unlocked phase with stable irregular dynamics and partial synchronization mechanisms.

Findings

Identification of two distinct dynamical phases based on coupling strength.

Discovery of a stable, irregular, unlocked phase with exponential ISI distribution.

Demonstration of external signal-induced synchronization of a neural subset.

Abstract

The dynamical properties of a diluted fully-inhibitory network of pulse-coupled neurons are investigated. Depending on the coupling strength, two different phases can be observed. At low coupling the evolution rapidly converges towards periodic attractors where all neurons fire with the same rate. At larger couplings, a new phase emerges, where all neurons are mutually unlocked. The irregular behaviour turns out to be "confined" to an exponentially long, stationary and linearly stable transient. In this latter phase we also find an exponentially tailed distribution of the inter-spike intervals (ISIs). Finally, we show that in the unlocked phase a subset of the neurons can be eventually synchronized under the action of an external signal, the remaining part of the neurons acting as a background noise. The dynamics of these ''background'' neurons is quite peculiar, in that it reveals a broad ISI distribution with a coefficient of variation that is close to 1.