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Gaussian mixture models and semantic gating improve reconstructions from human brain activity.


ABSTRACT: Better acquisition protocols and analysis techniques are making it possible to use fMRI to obtain highly detailed visualizations of brain processes. In particular we focus on the reconstruction of natural images from BOLD responses in visual cortex. We expand our linear Gaussian framework for percept decoding with Gaussian mixture models to better represent the prior distribution of natural images. Reconstruction of such images then boils down to probabilistic inference in a hybrid Bayesian network. In our set-up, different mixture components correspond to different character categories. Our framework can automatically infer higher-order semantic categories from lower-level brain areas. Furthermore, the framework can gate semantic information from higher-order brain areas to enforce the correct category during reconstruction. When categorical information is not available, we show that automatically learned clusters in the data give a similar improvement in reconstruction. The hybrid Bayesian network leads to highly accurate reconstructions in both supervised and unsupervised settings.

SUBMITTER: Schoenmakers S 

PROVIDER: S-EPMC4311641 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Gaussian mixture models and semantic gating improve reconstructions from human brain activity.

Schoenmakers Sanne S   Güçlü Umut U   Güçlü Umut U   van Gerven Marcel M   Heskes Tom T  

Frontiers in computational neuroscience 20150130


Better acquisition protocols and analysis techniques are making it possible to use fMRI to obtain highly detailed visualizations of brain processes. In particular we focus on the reconstruction of natural images from BOLD responses in visual cortex. We expand our linear Gaussian framework for percept decoding with Gaussian mixture models to better represent the prior distribution of natural images. Reconstruction of such images then boils down to probabilistic inference in a hybrid Bayesian netw  ...[more]

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