Name
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. |
Year | Venue | Field |
|---|---|---|
2017 | neural information processing systems | Pattern recognition,Functional magnetic resonance imaging,Convolutional neural network,Linear map,Artificial intelligence,Neural decoding,Maximum a posteriori estimation,Deep learning,Stimulus (physiology),Machine learning,Mathematics,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1705.07109 | 1 |
PageRank | References | Authors |
0.34 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yagmur Güçlütürk | 1 | 32 | 4.77 |
| Umut Güçlü | 2 | 88 | 10.86 |
| Katja Seeliger | 3 | 19 | 2.57 |
| Sander Bosch | 4 | 14 | 1.72 |
| Rob van Lier | 5 | 15 | 2.35 |
| Marcel Van Gerven | 6 | 321 | 39.35 |