Deep learning to probe neural correlates of music processing

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dc.contributor.advisorGerven, M.A.J. van
dc.contributor.advisorGuclu, U.
dc.contributor.authorThielen, J.
dc.date.issued2016-08-22
dc.description.abstractwith increasingly more complex features represented at more downstream areas along the cortical sheet. This has been best known for the visual cortex, but not so much for the auditory cortex. Recent advances in artificial neural networks allow the end-to-end learning of models for solving problems such as automated music tag prediction. Here, we trained a residual neural network to predict tags of natural music stimuli. In turn, the trained model was used to probe neural representations of music across the cortical sheet. Using a searchlight representational similarity analysis we revealed a representational gradient across the Superior Temporal Gyrus (STG). This gradient extended from Planum Polare, which was more sensitive to complex feature representations, to central STG, which was more sensitive to simple feature representations, to Planum Temporale, which was again more sensitive to complex features. The results imply low-level processing around primary auditory cortex with a broad auditory association area around it along STG. Keywords: deep learning, music processing, functional magnetic resonance imaging, representational similarity analysisen_US
dc.identifier.urihttp://hdl.handle.net/123456789/2636
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleDeep learning to probe neural correlates of music processingen_US
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