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Nat Neurosci. 2016 Mar;19(3):356-65. doi: 10.1038/nn.4244.

Using goal-driven deep learning models to understand sensory cortex.

Author information

1
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
2
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

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.

PMID:
26906502
DOI:
10.1038/nn.4244
[Indexed for MEDLINE]

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