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Title: Gaussian mixture models improve fMRI-based image reconstruction
Author(s): Schoenmakers, S. ; Güçlü, U. ; Heskes, T. ; Gerven, M.A.J. van
Publication year: 2014
In: PRNI 2014: 2014 International Workshop on Pattern Recognition in Neuroimaging, Tübingen, June 4-6 2014 : Proceedings, pp. 1-4
Publisher: Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE)
ISBN: 9781479941490
DOI: https://doi.org/10.1109/PRNI.2014.6858542
Publication type: Part of book or chapter of book

Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/132819   https://hdl.handle.net/2066/132819

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Subject: Biophysics
Cognitive artificial intelligence
DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Data Science
Organization: SW OZ DCC KI
Data Science
Donders Centre for Neuroscience
Book title: PRNI 2014: 2014 International Workshop on Pattern Recognition in Neuroimaging, Tübingen, June 4-6 2014 : Proceedings
Page start: p. 1
Page end: p. 4
Abstract:
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.
Languages used: English (eng)

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