Deep adversarial neural decoding
This addresses the challenge of decoding brain activity into visual stimuli, with potential applications in neuroscience and brain-computer interfaces, though it appears incremental as it builds on existing methods.
The paper tackles the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning, achieving state-of-the-art reconstructions of perceived faces from fMRI data.
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.