Gaussian mixture models improve fMRI-based image reconstruction
until further notice
Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE)
InPRNI 2014: 2014 International Workshop on Pattern Recognition in Neuroimaging, Tübingen, June 4-6 2014 : Proceedings, pp. 1-4
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SW OZ DCC KI
Donders Centre for Neuroscience
PRNI 2014: 2014 International Workshop on Pattern Recognition in Neuroimaging, Tübingen, June 4-6 2014 : Proceedings
SubjectBiophysics; Cognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; Data Science
New computational models have made it possible to reconstruct perceived images from BOLD responses in visual cortex. We expand a linear Gaussian framework for percept decoding with Gaussian mixture models to better represent the prior distribution of images. In our setup, different mixture components correspond to different letter categories. Our framework not only leads to more accurate reconstructions, but also automatically infers semantic categories from low-level visual areas of the human brain.
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