Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway

TL;DR

This study uses deep neural networks to demonstrate a gradient of increasing feature complexity along the human ventral visual pathway and improves decoding accuracy of brain representations.

Contribution

It introduces an automated method to map stimulus feature complexity across the human cortex using deep convolutional neural networks.

Findings

Gradient of feature complexity mapped across ventral pathway

Enhanced decoding accuracy of brain representations

Automated cortical mapping of stimulus features

Abstract

Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas. Using deep convolutional neural networks, we can now quantitatively demonstrate that there is indeed an explicit gradient for feature complexity in the ventral pathway of the human brain. Our approach also allows stimulus features of increasing complexity to be mapped across the human brain, providing an automated approach to probing how representations are mapped across the cortical sheet. Finally, it is shown that deep convolutional neural networks allow decoding of representations in the human brain at a previously unattainable degree of accuracy, providing a more sensitive window into the human brain.