Mapping of convolutional neural network activation maps on the visual cortex using a Bayesian linear framework

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dc.contributor.advisorGerven, M.A.J. van
dc.contributor.advisorSchoenmakers, S.
dc.contributor.authorBulk, L.M. van den
dc.date.issued2016-03-29
dc.description.abstractDecoding the brain is slowly starting to become a reality. Recently, new computational models have made it possible to make reconstructions of perceived images from BOLD responses in the visual cortex. A linear Gaussian framework was used by a previous study to decode functional magnetic resonance imaging data from subjects who were presented with images of handwritten characters. Here we expand this research by taking convolutional neural network activation maps instead of images of handwritten characters as the encoding and decoding stimuli as the di erent layers in the network appear to be good models for the di erent layers in the visual cortex. In contrast to former studies, a uniform prior is used to let the decoding be driven by the found encoding only. This approach results in good classi cation accuracies that become better as the convolutional layer becomes deeper and are more robust than previously found results.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2616
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleMapping of convolutional neural network activation maps on the visual cortex using a Bayesian linear frameworken_US
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