Modeling Biological Multifunctionality with Echo State Networks

TL;DR

This paper develops a reaction-diffusion model mimicking biological electrophysiological processes and demonstrates that Echo State Networks can effectively learn and reproduce its complex spatiotemporal dynamics.

Contribution

It introduces a novel combination of reaction-diffusion modeling with ESN training to simulate biological multifunctionality.

Findings

ESN successfully reproduces complex biological dynamics

Model captures key features of electrophysiological processes

Data-driven approach is effective for biological system simulation

Abstract

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.