A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is
We explore a straightforward method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimuli presented during two functional magnetic resonance imaging experiments. Using a linear model we learned to predict the generative model's latent random vector z from measured brain activity. The objective was to create an image similar to thedoi:10.1016/j.neuroimage.2018.07.043 pmid:30031932 fatcat:labmhjs5obdt3n45wuhsctjepe