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Modeling Biological Multifunctionality with Echo State Networks

arXiv 2510.23940 ↗First posted 27 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

In this work, a three-dimensional multicomponent reaction-diffusion model has been developed, combining excitable-system dynamics with diffusion processes and sharing conceptual features with the FitzHugh-Nagumo model. Designed to capture the spatiotemporal behavior of biological systems, particularly electrophysiological processes, the model was solved numerically to generate time-series data. These data were subsequently used to train and evaluate an Echo State Network (ESN), which successfully reproduced the system's dynamic behavior. The results demonstrate that simulating biological dynamics using data-driven, multifunctional ESN models is both feasible and effective.

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References · 4

  1. 2020Physics-Informed Echo State Networkscited by 16
  2. 2020A Review of Designs and Applications of Echo State Networkscited by 11
  3. 2017Detection of Generalized Synchronization using Echo State Networkscited by 3
  4. 2024Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networkscited by 2

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  1. 2025Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective
  2. 2023Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
  3. 2023Exploring Complex Dynamical Systems via Nonconvex Optimization
  4. 2026Physics-Informed Neural Networks for Biological 2D+t Reaction-Diffusion Systems
  5. 2025State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
  6. 2024BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis
  7. 2024Graph Neural Reaction Diffusion Models
  8. 2022Neural Lagrangian Schr"odinger Bridge: Diffusion Modeling for Population Dynamics