NeurIPS 2017poster91 citations
Reconstructing perceived faces from brain activations with deep adversarial neural decoding
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel A. J. van Gerven
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.
BibTeX
@inproceedings{NIPS2017_efdf562c,
author = {G\"{u}\c{c}l\"{u}t\"{u}rk, Ya\u{g}mur and G\"{u}\c{c}l\"{u}, Umut and Seeliger, Katja and Bosch, Sander and van Lier, Rob and van Gerven, Marcel A. J.},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Reconstructing perceived faces from brain activations with deep adversarial neural decoding},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/efdf562ce2fb0ad460fd8e9d33e57f57-Paper.pdf},
volume = {30},
year = {2017}
}