Self-induced stochastic resonance: A physics-informed machine learning approach

• URL: http://arxiv.org/abs/2510.22848v1
• Date: Sun, 26 Oct 2025 21:49:20 GMT
• Title: Self-induced stochastic resonance: A physics-informed machine learning approach
• Abstract summary: Self-induced resonance (SISR) is the emergence of coherent oscillations in excitable systems driven solely by noise.<n>This work presents a physics-informed machine learning framework for modeling and predicting SISR in the FitzHugh neuron.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-induced stochastic resonance (SISR) is the emergence of coherent oscillations in slow-fast excitable systems driven solely by noise, without external periodic forcing or proximity to a bifurcation. This work presents a physics-informed machine learning framework for modeling and predicting SISR in the stochastic FitzHugh-Nagumo neuron. We embed the governing stochastic differential equations and SISR-asymptotic timescale-matching constraints directly into a Physics-Informed Neural Network (PINN) based on a Noise-Augmented State Predictor architecture. The composite loss integrates data fidelity, dynamical residuals, and barrier-based physical constraints derived from Kramers' escape theory. The trained PINN accurately predicts the dependence of spike-train coherence on noise intensity, excitability, and timescale separation, matching results from direct stochastic simulations with substantial improvements in accuracy and generalization compared with purely data-driven methods, while requiring significantly less computation. The framework provides a data-efficient and interpretable surrogate model for simulating and analyzing noise-induced coherence in multiscale stochastic systems.

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