RT Journal Article SR Electronic T1 CNN-based Encoding and Decoding of Visual Object Recognition in Space and Time JF bioRxiv FD Cold Spring Harbor Laboratory SP 118091 DO 10.1101/118091 A1 K. Seeliger A1 M. Fritsche A1 U. Güçlü A1 S. Schoenmakers A1 J.-M. Schoffelen A1 S. E. Bosch A1 M. A. J. van Gerven YR 2017 UL http://biorxiv.org/content/early/2017/03/18/118091.abstract AB Deep convolutional neural networks (CNNs) have been put forward as neurobiologically plausible models of the visual hierarchy. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feedforward sweep across the visual hierarchy between 75-200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.