用神经可实现的傅里叶嵌入构建径向基核，支持连续空间表征。
Neurally-plausible radial basis kernels using distributed Fourier embeddings
- 基于分布式傅里叶嵌入构造神经可实现的径向基核
- 证明网格细胞类表征能最优实现径向基核
- 为物理与感知统一表征提供可解释框架
一致且连续的空间表征对于将物理和感知现象融合到统一表征空间至关重要。径向基核为此类分布式表征提供了一条可行路径。本文旨在分析在空间语义指针的神经可实现框架下，可实现的常见径向基核。进一步，我们分析了基于网格细胞类表征的先前径向基核工作，证明此类表征不仅能够实现径向基核，而且是实现该目标的最优方式。
原文摘要 · Abstract (English)
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.
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