Deep adversarial neural decoding
Neurons and Cognition
2017-06-16 v3 Machine Learning
Machine Learning
Abstract
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts the nonlinear transformation from perceived stimuli to latent features with adversarial training of convolutional neural networks. We test our approach with a functional magnetic resonance imaging experiment and show that it can generate state-of-the-art reconstructions of perceived faces from brain activations.
Keywords
Cite
@article{arxiv.1705.07109,
title = {Deep adversarial neural decoding},
author = {Yağmur Güçlütürk and Umut Güçlü and Katja Seeliger and Sander Bosch and Rob van Lier and Marcel van Gerven},
journal= {arXiv preprint arXiv:1705.07109},
year = {2017}
}
Comments
Added appendix and updated figures