Exposing the face manifold with generative modeling Reconstructing perceived faces from brain activations
| dc.contributor.advisor | Guclu, Umut | |
| dc.contributor.advisor | Gucluturk, Yagmur | |
| dc.contributor.author | Dado, Thirza | |
| dc.date.issued | 2020-02-01 | |
| dc.description.abstract | Neural encoding and neural decoding can shed light on (aspects of) the functional organization of visual processing in the brain. Here, we introduce a new framework for HYperrealistic reconstruction of PERceived naturalistic stimuli from brain recordings (HYPER). To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring functional magnetic resonance imaging data as subjects perceived face images created by the generator network of a GAN. Subsequently, we used a linear decoding approach to predict the latent state of the GAN from brain data. Hence, latent representations that are needed for stimulus (re-)generation are obtained, leading to ground-breaking image reconstructions. Furthermore, functional brain regions that encode for five visual features: gender, age, eyeglasses, pose, and smile, are localized using a multivariate searchlight, thereby identifying distributed networks of activity to be predictive of visual semantics of perceived stimuli. Altogether, we have developed a highly promising approach for decoding neural representations of real-world data, which may pave the way for systematically analyzing neural information processing in the functional brain. | en_US |
| dc.embargo.lift | 2045-02-01 | |
| dc.embargo.type | Tijdelijk embargo | en_US |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/10926 | |
| dc.language.iso | en | en_US |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
| dc.thesis.specialisation | Researchmaster Cognitive Neuroscience | en_US |
| dc.thesis.studyprogramme | Researchmaster Cognitive Neuroscience | en_US |
| dc.thesis.type | Researchmaster | en_US |
| dc.title | Exposing the face manifold with generative modeling Reconstructing perceived faces from brain activations | en_US |
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