Poster

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

Keywords: Deep Learning Predictive Models Supervised Deep Networks Deep Autoencoders Adversarial Networks Generative Models Perception Neuroscience and cognitive science Neuroscience Brain--Computer Interfaces and Neural Prostheses Neural Coding Brain Imaging

2017 Poster

[ Paper]

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.

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