Separability and geometry of object manifolds in deep neural networks.

ABSTRACT: Stimuli are represented in the brain by the collective population responses of sensory neurons, and an object presented under varying conditions gives rise to a collection of neural population responses called an 'object manifold'. Changes in the object representation along a hierarchical sensory system are associated with changes in the geometry of those manifolds, and recent theoretical progress connects this geometry with 'classification capacity', a quantitative measure of the ability to support object classification. Deep neural networks trained on object classification tasks are a natural testbed for the applicability of this relation. We show how classification capacity improves along the hierarchies of deep neural networks with different architectures. We demonstrate that changes i

SUBMITTER: Cohen U 

PROVIDER: S-EPMC7005295 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature