# Neurally-plausible radial basis kernels using distributed Fourier embeddings

arXiv: https://arxiv.org/abs/2605.08458 · HTML: https://mlsanity.com/paper/2605.08458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 1 of 1)

- [A short letter on the dot product between rotated Fourier transforms](https://mlsanity.com/paper/2007.13462/) (2020)

## Abstract (arXiv)

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.