Neurally-plausible radial basis kernels using distributed Fourier embeddings
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910203282194432 |
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| author | Chouinard, Jakeb |
| author_facet | Chouinard, Jakeb |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08458 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neurally-plausible radial basis kernels using distributed Fourier embeddings Chouinard, Jakeb Machine Learning Neurons and Cognition 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. |
| title | Neurally-plausible radial basis kernels using distributed Fourier embeddings |
| topic | Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2605.08458 |