{"ID":2849899,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23940","arxiv_id":"2510.23940","title":"Modeling Biological Multifunctionality with Echo State Networks","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.","short_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 electrophysiologi...","url_abs":"https://arxiv.org/abs/2510.23940","url_pdf":"https://arxiv.org/pdf/2510.23940v1","authors":"[\"Anastasia-Maria Leventi-Peetz\",\"Jörg-Volker Peetz\",\"Kai Weber\",\"Nikolaos Zacharis\"]","published":"2025-10-27T23:47:51Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Diffusion Model\"]","has_code":false}