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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

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #152 #None
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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: 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
[ Paper] 

Author Information

Yağmur Güçlütürk (Radboud University)
Umut Güçlü (Donders Institute)
Katja Seeliger (Donders Institute for Brain, Cognition and Behaviour)
Sander Bosch (Radboud University)
Rob van Lier (Donders Institute for Brain, Cognition and Behaviour, Radboud University)
Marcel A. J. van Gerven (Radboud Universiteit)

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