Signal integration enhances the dynamic range in neuronal systems

TL;DR

This paper explores how integrating multiple signals in neuronal networks enhances their dynamic range, revealing a discontinuous phase transition and bistability that improve stimulus discrimination capabilities.

Contribution

It demonstrates that signal integration induces a discontinuous phase transition and significantly enhances the dynamic range in excitable neuronal networks.

Findings

Discontinuous phase transition depends on integration time and unit density.

External stimuli induce bistability, boosting dynamic range.

Numerical and mean-field analyses confirm these effects.

Abstract

The dynamic range measures the capacity of a system to discriminate the intensity of an external stimulus. Such an ability is fundamental for living beings to survive: to leverage resources and to avoid danger. Consequently, the larger is the dynamic range, the greater is the probability of survival. We investigate how the integration of different input signals affects the dynamic range, and in general the collective behavior of a network of excitable units. By means of numerical simulations and a mean-field approach, we explore the nonequilibrium phase transition in the presence of integration. We show that the firing rate in random and scale-free networks undergoes a discontinuous phase transition depending on both the integration time and the density of integrator units. Moreover, in the presence of external stimuli, we find that a system of excitable integrator units operating in a bistable regime largely enhances its dynamic range.