  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.

Neurons and Cognition;Artificial Intelligence;Machine Learning;Quantitative Methods