Investigating the intrinsic top-down dynamics of deep generative models.

ABSTRACT: Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity at rest is the manifestation of top-down dynamics of generative models detached from action-perception cycles. A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms. In this work, we study the generative dynamics of a recent extension of the DBN, the iterative DBN (iDBN), which more faithfully simulates neurocognitive development by j

SUBMITTER: Tausani L 

PROVIDER: S-EPMC11754800 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature