@article {26, title = {Using goal-driven deep learning models to understand sensory cortex}, journal = {Nature Neuroscience}, volume = {19}, year = {2016}, month = {01/2016}, pages = {356 - 365}, abstract = {

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.

}, issn = {1097-6256}, doi = {10.1038/nn.4244}, url = {http://www.nature.com/articles/nn.4244.pdf}, author = {Yamins, Daniel L K and DiCarlo, James J} }