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

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. W...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 198; pp. 125 - 136
Main Authors:	Han, Kuan, Wen, Haiguang, Shi, Junxing, Lu, Kun-Han, Zhang, Yizhen, Fu, Di, Liu, Zhongming
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.09.2019 Elsevier Limited
Subjects:	Adult Artificial intelligence Bayesian brain Brain Mapping - methods Computer applications Deep learning Economic models Female Functional magnetic resonance imaging Humans Hypotheses Image Processing, Computer-Assisted Magnetic Resonance Imaging Models, Neurological Neural coding Neural networks Neural Networks, Computer Neuroimaging Neurosciences Pattern Recognition, Visual - physiology Random variables Supervision Unsupervised Machine Learning Variational autoencoder Visual cortex Visual Cortex - physiology Visual discrimination learning Visual reconstruction Young Adult Neural coding Variational autoencoder Bayesian brain Visual reconstruction
ISSN:	1053-8119, 1095-9572, 1095-9572
Online Access:	Get full text
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