Double coherence resonance in neuron models driven by discrete correlated noise

TL;DR

This paper investigates how correlations among discrete stochastic inputs affect the regularity of neuron responses, revealing a phenomenon called Double Coherence Resonance where optimal noise and correlation maximize signal coherence.

Contribution

It introduces the concept of Double Coherence Resonance in neuron models driven by correlated discrete noise, highlighting the role of input discreteness in neural response regularity.

Findings

Maximal regularity at finite noise levels for any correlation

Observation of Double Coherence Resonance with optimal noise and correlation

Discrete input nature explains the resonance effects

Abstract

We study the influence of correlations among discrete stochastic excitatory or inhibitory inputs on the response of the FitzHugh-Nagumo neuron model. For any level of correlation the emitted signal exhibits at some finite noise intensity a maximal degree of regularity, i.e., a coherence resonance. Furthermore, for either inhibitory or excitatory correlated stimuli a {\it Double Coherence Resonance} (DCR) is observable. DCR refers to a (absolute) maximum coherence in the output occurring for an optimal combination of noise variance and correlation. All these effects can be explained by taking advantage of the discrete nature of the correlated inputs.