Deep Neural Networks Reveal a Gradient in the Complexity of Neural
Representations across the Brain's Ventral Visual Pathway
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by
Umut Güçlü, Marcel A. J. van Gerven
2014
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.
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