The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We consider the problem of predicting brain activation in response to arbitrary words in English. Whereas previous computational models have encoded words using predefined sets of features, we formulate a model that can automatically learn features directly from data. We show that our model reduces to a simultaneous sparse approximation problem and show two examples where learned features give insight about how the brain represents meanings of words.

Keywords

sparse approximation, fMRI

Notes

Text Reference

Mark Palatucci, Tom Mitchell, and Han Liu, "Discovering a Semantic Basis of Neural Activity Using Simultaneous Sparse Approximation," International Conference on Machine Learning, Sparse Optimization and Variable Selection Workshop, July, 2008.

BibTeX Reference

@inproceedings{Palatucci_2008_6391,
   author = "Mark Palatucci and Tom Mitchell and Han Liu",
   title = "Discovering a Semantic Basis of Neural Activity Using Simultaneous Sparse Approximation",
   booktitle = "International Conference on Machine Learning, Sparse Optimization and Variable Selection Workshop",
   month = "July",
   year = "2008",
}

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