Gaussian Mixture Models Improve fMRI-based Image Reconstruction

被引：0

作者：

Schoenmakers, Sanne ^{[1
,3
]}

van Gerven, Marcel

Heskes, Tom ^{[2
]}

机构：

[1] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Donders Ctr Cognit, POB 9104, NL-6500 HE Nijmegen, Netherlands

[2] Radboud Univ Nijmegen, Inst Comp & Informat Sci, NL-6500 HE Nijmegen, Netherlands

[3] Radboud Univ Nijmegen, Donders Ctr Cognit, Donders Inst Brain Cognit & Behaviour, NL-6500 HE Nijmegen, Netherlands

来源：

2014 INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION IN NEUROIMAGING | 2014年

关键词：

BRAIN; REPRESENTATIONS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：4

共 12 条

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