RT Journal Article SR Electronic T1 Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 032623 DO 10.1101/032623 A1 Radoslaw Martin Cichy A1 Aditya Khosla A1 Dimitrios Pantazis A1 Aude Oliva YR 2015 UL http://biorxiv.org/content/early/2015/11/23/032623.abstract AB Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and a novel quantitative model of how spatial layout representations may emerge in the human brain.