Savaliya D, Yamakou M (2026)
Publication Type: Journal article
Publication year: 2026
Book Volume: 207
Article Number: 117998
DOI: 10.1016/j.chaos.2026.117998
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, 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.
APA:
Savaliya, D., & Yamakou, M. (2026). Self-induced stochastic resonance: A physics-informed machine learning approach. Chaos Solitons & Fractals, 207. https://doi.org/10.1016/j.chaos.2026.117998
MLA:
Savaliya, Divyesh, and Marius Yamakou. "Self-induced stochastic resonance: A physics-informed machine learning approach." Chaos Solitons & Fractals 207 (2026).
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