Coherence and stochastic resonance in a two-state system

Area of Science:

• Nonlinear dynamics
• Statistical physics
• Computational neuroscience

Background:

• The study investigates a piecewise linear FitzHugh-Nagumo model, a simplified neural model.
• The system exhibits bistable, excitable, or oscillatory behaviors depending on parameters.
• Focus is on two-state dynamics under Gaussian white noise and a weak harmonic signal.

Purpose of the Study:

• To characterize coherence resonance in bistable and excitable regimes.
• To quantify non-adiabatic resonances with respect to an external signal.
• To analyze output spectra and spectral power amplification for arbitrary noise and frequency.

Main Methods:

• Analysis of a piecewise linear FitzHugh-Nagumo model with perfect time scale separation.
• Calculation of output spectra and spectral power amplification.
• Investigation of system dynamics driven by Gaussian white noise and a harmonic signal.

Main Results:

• The system's behavior (bistable, excitable, oscillatory) is dependent on noise and signal parameters.
• Coherence resonance is characterized in specific regimes.
• Non-adiabatic resonances are quantified across all regimes.

Conclusions:

• The study provides a comprehensive analysis of resonance phenomena in a simplified neural model.
• Understanding these resonances is crucial for signal processing in noisy biological systems.
• The framework allows for arbitrary noise strengths and frequencies, offering broad applicability.