Testing geometric representation hypotheses from simulated place cell recordings

Thibault Niederhauser, Adam Lester, Nina Miolane, Khanh Dao Duc, Manu Madhav

Keywords: Place Cells, Task-Specific Encoding, Representation Learning, Autoencoders

Abstract:

Hippocampal place cells can encode spatial locations of an animal in physical or task-
relevant spaces. We simulated place cell populations that encoded either Euclidean- or
graph-based positions of a rat navigating to goal nodes in a maze with a graph topology,
and used manifold learning methods such as UMAP and Autoencoders (AE) to analyze
these neural population activities. The structure of the latent spaces learned by the AE
reflects their true geometric structure, while PCA fails to do so and UMAP is less robust to
noise. Our results support future applications of AE architectures to decipher the geometry
of spatial encoding in the brain.