Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

ABSTRACT: Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response,

SUBMITTER: Wen H 

PROVIDER: S-EPMC6215471 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature