Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.

ABSTRACT: Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual are

SUBMITTER: Han K 

PROVIDER: S-EPMC6592726 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature