Neural Decoding with Normalizing Flows
| dc.contributor.advisor | Guclu, Umut | |
| dc.contributor.advisor | Gerven, van, Marcel | |
| dc.contributor.author | Mahner, Florian | |
| dc.date.issued | 2021-05-01 | |
| dc.description.abstract | e pattern of light that reaches the retina is projected onto patterns of neural activity via a cascade of re exive computations alongside six interconnected brain areas in the occipital lobe called the visual ventral stream. e complex hierarchical architecture of this visual streamallows for e cient processing of visual information in the human brain. Machine learning has provided a powerful tool for encoding and decoding between brain responses and perceptual content. is thesis probes the use of generative ows as models of neural patterns from the perspective of decoding. We introduce a Neural Flowmodel that can bijectivelymap between a topographical interpretation of brain activity and the original input stimulus for a large-scale fMRI dataset of the BBC series Doctor Who. is approach is unique, as it inherently allows to combine neural encoding and decoding in a single neural network model. Our results show that it is possible to use this model to reconstruct simulated brain activity. For this, we applied the receptive eld estimators directly on the input frames. is imitates a noise-free representation of brain data and mimics the sparsity created within the encoding scheme. rough this, we show that the model successfully achieves to extrapolate from missing spatial information. We further nd through several ablation experiments on the simulation data that the model is robust to sparse training data sizes and to sparse receptive eld information. Across all ablation experiments, we nd strong positive correlations for the reconstructed images. e reconstructions on actual brain data found in this thesis match the previous benchmark results obtained on the same dataset. We achieve this, while at the same time including activity from only single regions of interest of brain activity, with leading performances for early visual areas VÕ and Vó. Overall, we nd that our normalizing ow model succesfully allows to reconstruct brain activity, while contributing a uni ed approach to neural encoding and decoding. | |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/16100 | |
| dc.language.iso | en | |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | |
| dc.thesis.specialisation | specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Master Artificial Intelligence | |
| dc.thesis.studyprogramme | studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence | |
| dc.thesis.type | Master | |
| dc.title | Neural Decoding with Normalizing Flows |
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