Unsupervised neural network models of the ventral visual stream.

ABSTRACT: Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods and that the mapping of these neural network models' hidden

SUBMITTER: Zhuang C 

PROVIDER: S-EPMC7826371 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature