Characterizing the temporal dynamics of object recognition by deep neural networks: role of depth

Convolutional neural networks (CNNs) have recently emerged as promising models of human vision based on their ability to predict hemodynamic brain responses to visual stimuli measured with functional magnetic resonance imaging (fMRI). However, the degree to which CNNs can predict temporal dynamics of visual object recognition reflected in neural measures with millisecond precision is less understood. Additionally, while deeper CNNs with higher numbers of layers perform better on automated peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/178541 doi: bioRxiv preprint first posted online Sep. 10, 2017;