Unsupervised feature learning improves prediction of human brain activity in response to natural images
Source
Plos Computational Biology, 10, 8, (2014), article e1003724ISSN
Publication type
Article / Letter to editor

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Organization
SW OZ DCC AI
Journal title
Plos Computational Biology
Volume
vol. 10
Issue
iss. 8
Languages used
English (eng)
Subject
Cognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal CommunicationAbstract
Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.
This item appears in the following Collection(s)
- Academic publications [227683]
- Electronic publications [107287]
- Faculty of Social Sciences [28418]
- Open Access publications [76415]
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