Testing geometric representation hypotheses from simulated place cell recordings

TL;DR

This study uses simulated hippocampal place cell data and manifold learning techniques to investigate how neural populations encode spatial information, highlighting autoencoders' effectiveness in capturing geometric structures.

Contribution

It demonstrates that autoencoders can accurately reflect the true geometric structure of neural encoding, outperforming PCA and UMAP in robustness and fidelity.

Findings

Autoencoders capture true geometric structures in neural data.

PCA fails to reflect the underlying geometry.

UMAP is less robust to noise in this context.

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.