%0 Journal Article %J Nature Neuroscience %D 2016 %T Using goal-driven deep learning models to understand sensory cortex %A Yamins, Daniel L K %A DiCarlo, James J %X

Fueled by innovation in the computer vision and artificial intelligence communities, recent developments in computational neuroscience have used goal-driven hierarchical convolutional neural networks (HCNNs) to make strides in modeling neural single-unit and population responses in higher visual cortical areas. In this Perspective, we review the recent progress in a broader modeling context and describe some of the key technical innovations that have supported it. We then outline how the goal-driven HCNN approach can be used to delve even more deeply into understanding the development and organization of sensory cortical processing.

%B Nature Neuroscience %V 19 %P 356 - 365 %8 01/2016 %G eng %U http://www.nature.com/articles/nn.4244.pdf %N 3 %! Nat Neurosci %R 10.1038/nn.4244