J
Jack L. Gallant
Researcher at University of California, Berkeley
Publications - 113
Citations - 16729
Jack L. Gallant is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Visual cortex & Receptive field. The author has an hindex of 52, co-authored 101 publications receiving 14642 citations. Previous affiliations of Jack L. Gallant include Helen Wills Neuroscience Institute & California Institute of Technology.
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Journal ArticleDOI
Identifying natural images from human brain activity.
TL;DR: A decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas is developed and it is suggested that it may soon be possible to reconstruct a picture of a person’s visual experience from measurements of brain activity alone.
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Sparse coding and decorrelation in primary visual cortex during natural vision.
William E. Vinje,Jack L. Gallant +1 more
TL;DR: Theoretical studies suggest that primary visual cortex (area V1) uses a sparse code to efficiently represent natural scenes, but this issue was investigated by recording from V1 neurons in awake behaving macaques during both free viewing of natural scenes and conditions simulating natural vision.
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Natural speech reveals the semantic maps that tile human cerebral cortex
Alexander G. Huth,Wendy A. de Heer,Thomas L. Griffiths,Thomas L. Griffiths,Frédéric E. Theunissen,Frédéric E. Theunissen,Jack L. Gallant,Jack L. Gallant +7 more
TL;DR: This study systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI data collected while subjects listened to hours of narrative stories, and uses a novel generative model to create a detailed semantic atlas.
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Reconstructing visual experiences from brain activity evoked by natural movies
Shinji Nishimoto,An T. Vu,Thomas Naselaris,Yuval Benjamini,Bin Yu,Jack L. Gallant,Jack L. Gallant +6 more
TL;DR: A new motion-energy encoding model is presented that largely overcomes the limitation of blood oxygen level-dependent signals measured via fMRI and demonstrates that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.
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A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain
TL;DR: This work used fMRI to measure human brain activity evoked by natural movies and voxelwise models to examine the cortical representation of 1,705 object and action categories to find a semantic space mapped smoothly across the cortical surface.