Stochastic resonance of a periodically driven neuron under non-Gaussian noise

J.R.R. Duarte, M.V.D. Vermelho and M.L. Lyra

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 7, 1446-1454

Abstract: We investigate the first-passage-time statistics of the integrate–fire neuron model driven by a sub-threshold harmonic signal superposed with a non-Gaussian noise. Here, we considered the noise as the result of a random multiplicative process displaced from the origin by an additive term. Such a mechanism generates a power-law distributed noise whose characteristic decay exponent can be finely tuned. We performed numerical simulations to analyze the influence of the noise non-Gaussian character on the stochastic resonance condition. We found that when the noise deviates from Gaussian statistics, the resonance condition occurs at weaker noise intensities, achieving a minimum at a finite value of the distribution function decay exponent. We discuss the possible relevance of this feature to the efficiency of the firing dynamics of biological neurons, as the present result indicates that neurons would require a lower noise level to detect a sub-threshold signal when its statistics departs from Gaussian.

Keywords: Stochastic resonance; Non-Gaussian noise; Integrate–fire model (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:7:p:1446-1454

DOI: 10.1016/j.physa.2007.11.011

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