用回声状态网络模拟生物多功能动态行为，效果良好。
Modeling Biological Multifunctionality with Echo State Networks
- 构建三维反应扩散模型，融合激发系统与扩散机制
- 训练的回声状态网络成功复现了系统动态行为
- 适合研究生物电生理过程的科研人员参考
本文提出一种三维多组分反应-扩散模型，结合激发系统动力学与扩散过程，具有与FitzHugh-Nagumo模型相似的概念特征。该模型用于捕捉生物系统的时空行为，特别是电生理过程，通过数值求解生成时间序列数据。这些数据被用于训练和评估回声状态网络（ESN），结果表明该网络能有效再现系统的动态行为。研究证明，基于数据驱动的多功能ESN模型在模拟生物动力学方面具有可行性和有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。