Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

J Neurosci. 2015 Jul 8;35(27):10005-14. doi: 10.1523/JNEUROSCI.5023-14.2015.

Abstract

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.

Keywords: deep learning; functional magnetic resonance imaging; neural coding.

MeSH terms

  • Brain / blood supply
  • Brain / physiology*
  • Brain Mapping*
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male
  • Models, Neurological*
  • Oxygen / blood
  • Photic Stimulation
  • Visual Pathways / blood supply
  • Visual Pathways / physiology*

Substances

  • Oxygen