Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images

PLoS Computational Biology

Volumn 10, Issue 8, 2014, Pages

  Güçlü, Umut ^a   van Gerven, Marcel A J ^a  

^a RADBOUD UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; COMPUTATION THEORY; IMAGE ENHANCEMENT; MAGNETIC RESONANCE IMAGING; NEUROPHYSIOLOGY; POPULATION STATISTICS;

BRAIN ACTIVITY; COMPUTATIONAL MODELLING; ENCODING AND DECODING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN BRAIN; NATURAL IMAGES; SINGLE VOXEL; UNLABELED DATA; UNSUPERVISED FEATURE LEARNING; VISUAL CORTICAL;

FORECASTING;

BRAIN FUNCTION; HUMAN; LEARNING; MODEL; POPULATION MODEL; PREDICTION; STIMULUS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; FUNCTIONAL NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; VISUAL CORTEX;

ARTIFICIAL INTELLIGENCE; FUNCTIONAL NEUROIMAGING; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; VISUAL CORTEX;

EID: 84928704499     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003724     Document Type: Article

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