Bayesian reconstruction of natural images from human brain activity.
Recent studies have used fMRI signals from early visual areas to reconstruct simple geometric patterns. Here, we demonstrate a new Bayesian decoder that uses fMRI signals from early and anterior visual areas to reconstruct complex natural images. Our decoder combines three elements: a structural encoding model that characterizes responses in early visual areas, a semantic encoding model that characterizes responses in anterior visual areas, and prior information about the structure and semantic content of natural images. By combining all these elements, the decoder produces reconstructions that accurately reflect both the spatial structure and semantic category of the objects contained in the observed natural image. Our results show that prior information has a substantial effect on the quality of natural image reconstructions. We also demonstrate that much of the variance in the responses of anterior visual areas to complex natural images is explained by the semantic category of the image alone.
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
Thomas
T
Naselaris
Ryan J
RJ
Prenger
Kendrick N
KN
Kay
Michael
M
Oliver
Jack L
JL
Gallant
D001499:Bayes Theorem; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008959:Models, Neurological; D010100:Oxygen / Q000097:blood; D010775:Photic Stimulation / Q000379:methods; D011601:Psychophysics; D012660:Semantics; D014793:Visual Cortex / Q000033:anatomy & histology* / Q000098:blood supply / Q000502:physiology; D014796:Visual Perception
D010100:Oxygen