Neurally-plausible radial basis kernels using distributed Fourier embeddings
Machine Learning
2026-05-12 v1 Neurons and Cognition
Abstract
Coherent, continuous spatial representations are critical for synthesizing physical and perceptual phenomena into a single representational space. Radial basis kernels provide a path forward for this type of distributed representation. In this work, we aim to characterize and analyze common radial basis kernels realizable in the neurally-plausible framework of spatial semantic pointers. Further, we analyze previous radial basis kernel work based on grid cell-like representations and demonstrate that such representations are both capable of and optimal for realizing radial basis kernels.
Keywords
Cite
@article{arxiv.2605.08458,
title = {Neurally-plausible radial basis kernels using distributed Fourier embeddings},
author = {Jakeb Chouinard},
journal= {arXiv preprint arXiv:2605.08458},
year = {2026}
}