Bayesian Reconstruction of Natural Images from Human Brain Activity Export

@article{Naselaris_2009_bayesian, abstract = {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.}, author = {Naselaris, Thomas and Prenger, Ryan J. and Kay, Kendrick N. and Oliver, Michael and Gallant, Jack L.}, citeulike-article-id = {5843260}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neuron.2009.09.006}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0896627309006850}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19778517}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19778517}, day = {24}, doi = {10.1016/j.neuron.2009.09.006}, issn = {08966273}, journal = {Neuron}, keywords = {bayesian, coding, consciousness, decoding, fmri, visual}, month = {September}, number = {6}, pages = {902--915}, posted-at = {2009-10-27 08:30:28}, priority = {2}, title = {Bayesian Reconstruction of Natural Images from Human Brain Activity}, url = {http://dx.doi.org/10.1016/j.neuron.2009.09.006}, volume = {63}, year = {2009} }

TY - JOUR ID - Naselaris_2009_bayesian L3 - citeulike-article-id:5843260 N2 - 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. IS - 6 JF - Neuron SN - 08966273 EP - 915 TI - Bayesian Reconstruction of Natural Images from Human Brain Activity VL - 63 SP - 902 KW - bayesian KW - coding KW - consciousness KW - decoding KW - fmri KW - visual AU - Naselaris, Thomas AU - Prenger, Ryan AU - Kay, Kendrick AU - Oliver, Michael AU - Gallant, Jack PY - 2009/September/24/ UR - http://dx.doi.org/10.1016/j.neuron.2009.09.006 ER -