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The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of humandoi:10.1038/srep27755 pmid:27282108 pmcid:PMC4901271 fatcat:ijfwlcbs4zgr3li44ttzt74mjy