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

arXiv 2510.22848 ↗First posted 26 Oct 2025cs.LG nlin.AO stat.MLcited by 0

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Abstract (arXiv)

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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References · 18

  1. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  2. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
  3. 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 629
  4. 2019DeepXDE: A deep learning library for solving differential equationscited by 617
  5. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  6. 2020Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanismcited by 231
  7. 2021Efficient and Accurate Gradients for Neural SDEscited by 68
  8. 2021The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?cited by 61
  9. 2021Discovering Sparse Interpretable Dynamics from Partial Observationscited by 25
  10. 2021Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama schemecited by 10
  11. 2021A deep learning based surrogate model for stochastic simulatorscited by 6
  12. 2024Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systemscited by 5
6 more
  1. 2018Functional importance of noise in neuronal information processingcited by 4
  2. 2024Stochastic Reservoir Computerscited by 4
  3. 2021Discovery of slow variables in a class of multiscale stochastic systems via neural networkscited by 2
  4. 2017Stochastic resonance and optimal information transfer at criticality on a network model of the human connectomecited by 1
  5. 2020Optimal self-induced stochastic resonance in multiplex neural networks: electrical versus chemical synapsescited by 1
  6. 2023Combined effects of STDP and homeostatic structural plasticity on coherence resonancecited by 1

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