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Neural Operators accelerate FitzHugh-Nagumo dynamics modeling

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了参数条件傅里叶神经网络算子（FNOs），为FitzHugh-Nagumo（FHN）系统创建了快速、可微分的代理模型。与传统求解器相比，这些模型能够以显著降低的计算成本精确模拟神经元电压动力学，包括兴奋性和振荡状态。FNOs在振荡状态下实现了低于0.1%的相对L2误差，并在兴奋状态下准确再现了触发阈值和传导速度等关键特征，展示了强大的泛化和外推能力。 AI

影响 通过实现对复杂生物系统的更快模拟，加速了科学发现。

排序理由 该集群包含一篇学术论文，详细介绍了使用神经网络模拟复杂动力学的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Neural Operators accelerate FitzHugh-Nagumo dynamics modeling

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该集群包含一篇学术论文，详细介绍了使用神经网络模拟复杂动力学的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andrew Franck, Justin Li ·

    参数化神经算子对兴奋性和振荡性FitzHugh-Nagumo动力学的快速代理建模

    arXiv:2609.04549v1 Announce Type: new Abstract: The FitzHugh-Nagumo (FHN) system serves as a simplified model of neuronal voltage dynamics, capturing the activator-inhibitor structure behind both isolated action potentials and the rhythmic spiking seen across the brain. Exploring…

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