Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

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 human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on…

• NS
  National Science Foundation

  Award: 1532591
• AV
  Alexander von Humboldt-Stiftung
• MI
  Massachusetts Institute of Technology
• DF
  Deutsche Forschungsgemeinschaft

  Award: CI 241/1-1
• MI
  McGovern Institute for Brain Research, Massachusetts Institute of Technology