🤖 AI Summary
This study addresses the challenge of constructing neurally plausible, coherent continuous-space representations that unify physical and perceptual information. Building upon the spatial semantic pointer framework, we propose a radial basis kernel implementation tailored for distributed representations and systematically analyze the role of grid-cell-like codes within this architecture. Both theoretical analysis and empirical results demonstrate that grid-cell-like representations not only naturally facilitate the construction of radial basis kernels but also exhibit optimality in terms of neural implementability. Our work is the first to integrate spatial semantic pointers, distributed Fourier embeddings, and grid-cell-like mechanisms into a unified model, yielding an efficient and biologically plausible spatial representation system that demonstrates significant advantages in representational capacity and neural implementation efficiency.
📝 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.