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Multiplexing-based control of stochastic resonance.

Vladimir V Semenov1, Anna Zakharova1

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Summary
This summary is machine-generated.

Multiplexing controls noise dynamics in multilayer networks, enhancing stochastic resonance when forcing and noise are in all layers. Optimal coupling strength and more layers amplify this effect.

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Area of Science:

  • Physics
  • Network Science
  • Nonlinear Dynamics

Background:

  • Stochastic resonance (SR) is a phenomenon where a weak signal can be amplified by adding noise.
  • Multilayer networks offer complex dynamics not seen in single-layer systems.
  • Controlling noise-induced dynamics in networks is crucial for understanding complex systems.

Purpose of the Study:

  • To investigate the role of multiplexing in controlling noise-induced dynamics within multilayer networks.
  • To explore how multiplexing affects stochastic resonance in networks of bistable oscillators.
  • To determine the conditions under which multiplexing enhances or suppresses stochastic resonance.

Main Methods:

  • Modeling multilayer networks of bistable overdamped oscillators.
  • Analyzing the signal-to-noise ratio (SNR) as a function of noise intensity.
  • Varying coupling strengths between network layers and the number of layers.
  • Simulating systems with and without periodic forcing in different layers.

Main Results:

  • Multiplexing suppresses stochastic resonance when periodic forcing is applied to only one layer.
  • Multiplexing enhances stochastic resonance when both periodic forcing and noise are present in all interacting layers.
  • The enhancement of stochastic resonance by multiplexing exhibits a resonant character with respect to coupling strength and increases with the number of layers.

Conclusions:

  • Multiplexing is a key factor in controlling noise-induced dynamics and stochastic resonance in multilayer networks.
  • The findings highlight the potential for designing network topologies to optimize signal detection in noisy environments.
  • This study provides insights into the interplay between network structure, noise, and signal processing in complex systems.