Levy noise-induced self-induced stochastic resonance in a memristive neuron

TL;DR

This paper demonstrates that Levy noise can induce self-induced stochastic resonance in memristive neurons, revealing new effects of non-Gaussian noise and memristive properties on neural resonance phenomena.

Contribution

It introduces Levy noise as a new factor in SISR, explores memristive effects on resonance, and compares Levy and Gaussian noise impacts in neural systems.

Findings

Levy noise can induce strong SISR in memristive neurons.

Memristive properties influence the degree of SISR differently.

Memristive neurons exhibit higher SISR than non-memristive neurons.

Abstract

Self-induced stochastic resonance (SISR) is a subtle resonance mechanism requiring a nontrivial scaling limit between the stochastic and the deterministic timescales of an excitable system, leading to the emergence of a limit cycle behavior which is absent without noise. All previous studies on SISR in neural systems have only considered the idealized Gaussian white noise. Moreover, these studies have ignored one electrophysiological aspect of the nerve cell: its memristive properties. In this paper, first, we show that in the excitable regime, the asymptotic matching of the Levy timescale (that follows a power law, unlike Gaussian noise that follows Kramers law) and the deterministic timescale (controlled by the singular parameter) can also induce a strong SISR. In addition, it is shown that the degree of SISR induced by Levy noise is not always higher than that of Gaussian noise. Second, we show that, for both types of noises, the two memristive properties of the neuron have opposite effects on the degree of SISR: the stronger the feedback gain parameter that controls the modulation of the membrane potential with the magnetic flux and the weaker the feedback gain parameter that controls the saturation of the magnetic flux, the higher the degree of SISR. Finally, we show that, for both types of noises, the degree of SISR in the memristive neuron is always higher than in the non-memristive neuron. Our results could find applications in designing neuromorphic circuits operating in noisy regimes.