Abstract

Stochastic resonance is a subtle, yet powerful phenomenon in which noise plays an interesting role of amplifying a signal instead of attenuating it. It has attracted great attention with a vast number of applications in physics, chemistry, biology, etc. Popular measures to study stochastic resonance include signal-to-noise ratios, residence time distributions, and different information theoretic measures. Here, we show that the information length provides a novel method to capture stochastic resonance. The information length measures the total number of statistically different states along the path of a system. Specifically, we consider the classical double-well model of stochastic resonance in which a particle in a potential V ( x , t ) = [ - x 2 / 2 + x 4 / 4 - A sin ( ω t ) x ] is subject to an additional stochastic forcing that causes it to occasionally jump between the two wells at x ≈ ± 1 . We present direct numerical solutions of the Fokker–Planck equation for the probability density function p ( x , t ) for ω = 10 - 2 to 10 - 6 , and A ∈ [ 0 , 0 . 2 ] and show that the information length shows a very clear signal of the resonance. That is, stochastic resonance is reflected in the total number of different statistical states that a system passes through.

Highlights

  • Mutual interaction between two systems is most effective when the time scales of the two match, i.e., at the resonance

  • The purpose of this paper is to introduce a new way of describing stochastic resonance from the perspective of information geometry

  • We show below that stochastic resonance is accompanied by a rapid change in Probability Density Function (PDF), generating a new source of information

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Summary

Introduction

Mutual interaction between two systems is most effective when the time scales of the two match, i.e., at the resonance. The purpose of this paper is to introduce a new way of describing stochastic resonance from the perspective of information geometry We do this by mapping the evolution of a stochastic system to a statistical space and interpret stochastic resonance in terms of the total number of statistically different states that a system passes through in time over a cycle of the modulation. The latter is quantified by Proceedings 2020, 46, 10; doi:10.3390/ecea-5-06667 www.mdpi.com/journal/proceedings. We show below that stochastic resonance is accompanied by a rapid change in PDF, generating a new source of information.

Double-Well Potential Model
Probability Density Functions
Escape Times
Information Length
Conclusions
Full Text
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