Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

ABSTRACT: The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN

SUBMITTER: Shi J 

PROVIDER: S-EPMC5895512 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature