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

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.

arXiv comment7 two-column pages, 5 figures; accepted for publication in Chaos

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Cited by in corpus (4)

• Spontaneous formation of synchronization clusters in homogenous neuronal ensembles induced by noise and interaction delays
• Spatiotemporal dynamics on small-world neuronal networks: The roles of two types of time-delayed coupling
• Auto-correlative weak-value amplification under strong noise background
• Signal transmission in a Y-shaped one-way chain