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.

利用深度卷积神经网络，研究人员成功地探测到哺乳动物腹侧视觉途径中逐渐增加的特定特征梯度，并将不断增加的特征复杂度映射到人类大脑，从而提供了研究大脑表示如何映射的自动化方法。最终，研究表明，在先前无法实现的精度下，深度卷积神经网络可以对人脑表示进行解码，为我们提供了更敏感的窗口观察人脑。