Nonstationary Stochastic Resonance in a Single Neuron-Like System

TL;DR

This paper demonstrates that a single neuron-like system can utilize stochastic resonance to detect weak, nonstationary signals, highlighting its potential relevance in biological and electronic systems.

Contribution

It shows that even a simple trigger mechanism can achieve stochastic resonance for nonstationary signals, extending the phenomenon's applicability to elementary neural models.

Findings

Single neuron-like system detects weak signals via stochastic resonance.

Modification of trigger mechanism enables nonstationary signal detection.

Relevance to biological and electronic systems with realistic signals.

Abstract

Stochastic resonance holds much promise for the detection of weak signals in the presence of relatively loud noise. Following the discovery of nondynamical and of aperiodic stochastic resonance, it was recently shown that the phenomenon can manifest itself even in the presence of nonstationary signals. This was found in a composite system of differentiated trigger mechanisms mounted in parallel, which suggests that it could be realized in some elementary neural networks or nonlinear electronic circuits. Here, we find that even an individual trigger system may be able to detect weak nonstationary signals using stochastic resonance. The very simple modification to the trigger mechanism that makes this possible is reminiscent of some aspects of actual neuron physics. Stochastic resonance may thus become relevant to more types of biological or electronic systems injected with an ever broader class of realistic signals.