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
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.
Chat is not available.
Successful Page Load