# Modeling Biological Multifunctionality with Echo State Networks

arXiv: https://arxiv.org/abs/2510.23940 · HTML: https://mlsanity.com/paper/2510.23940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Physics-Informed Echo State Networks](https://mlsanity.com/paper/2011.02280/) (2020)
- [A Review of Designs and Applications of Echo State Networks](https://mlsanity.com/paper/2012.02974/) (2020)
- [Detection of Generalized Synchronization using Echo State Networks](https://mlsanity.com/paper/1710.08286/) (2017)
- [Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks](https://mlsanity.com/paper/2401.07379/) (2024)

## 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.