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Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images

Ghislain St-Yves, Thomas Naselaris
doi: https://doi.org/10.1101/304774
Ghislain St-Yves
Medical University of South Carolina, Dept. of Neurosciences, 96 Jonathan-Lucas St. CSB 325c, Charleston, SC 29425 USA
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Thomas Naselaris
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Abstract

We consider the inference problem of reconstructing a visual stimulus from brain activity measurements (e.g. fMRI) that encode this stimulus. Recovering a complete image is complicated by the fact that neural representations are noisy, high-dimensional, and contain incomplete information about image details. Thus, reconstructions of complex images from brain activity require a strong prior. Here we propose to train generative adversarial networks (GANs) to learn a generative model of images that is conditioned on measurements of brain activity. We consider two challenges of this approach: First, given that GANs require far more data to train than is typically collected in an fMRI experiment, how do we obtain enough samples to train a GAN that is conditioned on brain activity? Secondly, how do we ensure that our generated samples are robust against noise present in fMRI data? Our strategy to surmount both of these problems centers around the creation of surrogate brain activity samples that are generated by an encoding model. We find that the generative model thus trained generalizes to real fRMI data measured during perception of images and is able to reconstruct the basic outline of the stimuli.

Footnotes

  • Email: stayves{at}musc.edu Email: tnaselar{at}musc.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 20, 2018.
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Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images
Ghislain St-Yves, Thomas Naselaris
bioRxiv 304774; doi: https://doi.org/10.1101/304774
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Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images
Ghislain St-Yves, Thomas Naselaris
bioRxiv 304774; doi: https://doi.org/10.1101/304774

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