<ul><li>Ionic models are crucial for simulating the dynamics of excitable cells in Computational Neuroscience and Cardiology.</li><li>Researchers are exploring the use of Fourier Neural Operators to learn the dynamics of state variables in high-dimensional ionic models.</li><li>Results show that Fourier Neural Operators can effectively capture the dynamics of models like FitzHugh-Nagumo, Hodgkin-Huxley, and O'Hara-Rudy.</li><li>Both constrained and unconstrained architectures of Fourier Neural Operators perform well in terms of accuracy, with unconstrained architecture requiring fewer training epochs.</li></ul>

Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators

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