Stochastic resonance

Stochastic resonance (SR) is a mathematical mechanism and behavior of nonlinear systems (that is, systems in which the change of the output is not proportional to the change of the input) where random (stochastic) fluctuations in the microstate of a system (that is, its specific configuration, including the precise positions and momenta of all its individual particles or components) cause deterministic (that is, non-random) changes in a macrostate (that is, a subset of the system's microstates). This occurs when the nonlinear nature of the system amplifies certain (resonant) portions of the fluctuations, while not amplifying other portions of the noise.

The nonlinear system, immersed in a certain level of stochastic background noise, becomes sensitive to external perturbations that would be too weak to influence it in the absence of such noise.

Originally proposed in the context of climate dynamics, over time it has become important in numerous fields that study a wide variety of systems, particularly in information theory and in neuroscience. Phenomena attributable to stochastic resonance have also been observed in other types of physical systems, such as chemical reactions, quantum systems, and industrial processes. Stochastic resonance is also closely related to the concept of dithering in signal analysis, although how similar or how different the two concepts are depends on the particular definition considered.

Stochastic resonance was discovered and proposed for the first time in 1981 to explain the periodic recurrence of ice ages. Nowadays stochastic resonance is commonly invoked when noise and nonlinearity concur to determine an increase of order in a system's response.

See the Review of Modern Physics article "Stochastic resonance" for a comprehensive overview of stochastic resonance.

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