Poster
in
Workshop: Symmetry and Geometry in Neural Representations (NeurReps)
Testing geometric representation hypotheses from simulated place cell recordings
Thibault Niederhauser · Adam Lester · Nina Miolane · Khanh Dao Duc · Manu Madhav
Keywords: [ Place Cells ] [ Representation Learning ] [ Autoencoders ] [ Task-Specific Encoding ]
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- orgraph-based positions of a rat in a maze apparatus, and used an Autoencoder (AE) toanalyze these neural population activities. The structure of the latent space learned by theAE reflects the respective geometric representation, while PCA fails to do so. This suggestsfuture applications of AE architectures to decipher the geometry of spatial encoding in thebrain.