Transferring and generalizing deep-learning-based neural encoding models across subjects.

ABSTRACT: Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population. In this study, we developed new methods to transfer and generalize encoding models across subjects. To train encoding models specific to a target subject, the models trained for other subjects were used as the prior models and were refined efficiently using Bayesian inference with a l

SUBMITTER: Wen H 

PROVIDER: S-EPMC5976558 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature