Preprint on Radial Basis Function Schemes for brain dynamics

Daniele Avitabile and collaborators from University of Colorado Boulder published a preprint which applies Radial Basis Function discretisation to neural field equations for brain dynamics.

Approximating schemes based on Radial Basis Functions (RBFs) are known for their flexibility and accuracy: RBF-based interpolation achieves high accuracy with a meshless approach, which allows to distribute interpolation nodes flexibly in multiple spatial dimensions.

In this work RBF interpolation is employed in neural fields, and the approach is adapted to integro-differential equations for brain dynamics. This paper opens up the possibility of coupling activity models to reaction-diffusion models on surfaces, for which RBFs are an attractive discretisation method.