Title | Using goal-driven deep learning models to understand sensory cortex |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Yamins, DLK, DiCarlo, JJ |
Journal | Nature Neuroscience |
Volume | 19 |
Issue | 3 |
Pagination | 356 - 365 |
Date Published | 01/2016 |
ISSN | 1097-6256 |
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. |
URL | http://www.nature.com/articles/nn.4244.pdf |
DOI | 10.1038/nn.4244 |
Short Title | Nat Neurosci |