English

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}
}
R2 v1 2026-07-01T12:59:02.776Z