Effects of correlated Gaussian noise on the mean firing rate and correlations of an electrically coupled neuronal network

TL;DR

This study investigates how correlated Gaussian noise influences the firing rate and neuron correlations in a modeled neuronal network, revealing optimal noise levels for firing rate and the impact of noise correlation on coherence.

Contribution

It provides new insights into the effects of noise correlation on neuronal activity and coherence in a two-dimensional network modeled by the Rulkov map.

Findings

Mean firing rate is optimized at intermediate noise levels.

Noise correlation affects population coherence significantly.

Local and global noise ratios influence network dynamics.

Abstract

In this paper, we examine the effects of correlated Gaussian noise on a two-dimensional neuronal network that is locally modeled by the Rulkov map. More precisely, we study the effects of the noise correlation on the variations of the mean firing rate and the correlations among neurons versus the noise intensity. Via numerical simulations, we show that the mean firing rate can always be optimized at an intermediate noise intensity, irrespective of the noise correlation. On the other hand, variations of the population coherence with respect to the noise intensity are strongly influenced by the ratio between local and global Gaussian noisy inputs. Biological implications of our findings are also discussed.