title: Using goal-driven deep learning models to understand sensory cortex.
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.
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Daniel L K
authors.initials: DL
authors.lastname: Yamins
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: DiCarlo
mesh_terms: D000818:Animals; D006040:Goals; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013003:Somatosensory Cortex / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*