Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23940 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914117739085824 |
|---|---|
| author | Leventi-Peetz, Anastasia-Maria Peetz, Jörg-Volker Weber, Kai Zacharis, Nikolaos |
| author_facet | Leventi-Peetz, Anastasia-Maria Peetz, Jörg-Volker Weber, Kai Zacharis, Nikolaos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Biological Multifunctionality with Echo State Networks Leventi-Peetz, Anastasia-Maria Peetz, Jörg-Volker Weber, Kai Zacharis, Nikolaos Machine Learning Artificial Intelligence 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. |
| title | Modeling Biological Multifunctionality with Echo State Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23940 |