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Detecting and quantifying stochastic and coherence resonances via information-theory complexity measurements
Osvaldo A Rosso1, Cristina Masoller
1Centre for Bioinformatics, School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, New South Wales 2308, Australia.
Summary
Statistical complexity measures detect noise-induced order and quantify resonances. This method precisely identifies subtle noise-induced order signatures in complex real-world signals.
Area of Science:
- Complex systems analysis
- Statistical physics
- Nonlinear dynamics
Background:
- Noise can induce order in complex systems, a phenomenon known as noise-induced order.
- Stochastic and coherence resonances are key mechanisms influencing system behavior under noise.
- Quantifying these effects is crucial for understanding complex signal dynamics.
Purpose of the Study:
- To introduce and validate a statistical complexity measure for detecting noise-induced order.
- To quantify stochastic and coherence resonances in complex systems.
- To demonstrate the method's applicability to real-world complex signals.
Main Methods:
- Application of statistical complexity measures.
- Modeling a Brownian particle in a modulated bistable potential.
- Utilizing the FitzHugh-Nagumo model for excitable systems.
Main Results:
- The statistical complexity measure successfully detects noise-induced order.
- The method quantifies stochastic and coherence resonances.
- Demonstrated effectiveness in paradigmatic models.
Conclusions:
- Statistical complexity offers a robust tool for analyzing noise-induced order.
- The method is effective for quantifying resonance phenomena.
- Applicable for precise detection of subtle noise-induced order signatures in real-world data.
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