High-performing neural network models of visual cortex benefit from high latent dimensionality.

ABSTRACT: Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the latent dimensionality of their natural image representations. A popular view holds that optimal DNNs compress their representations onto low-dimensional subspaces to achieve invariance and robustness, which suggests that better models of visual cortex should have lower dimensional geometries. Surprisingly, we found a strong trend in the opposite direction-neural networks with high-dimensional image subspaces tended to have better generalization performance when predicting cortical responses to held-out stimuli in both monkey electrophysiology and human fMR

SUBMITTER: Elmoznino E 

PROVIDER: S-EPMC10805290 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature