Journal
JOURNAL OF COGNITIVE NEUROSCIENCE
Volume 33, Issue 10, Pages 2017-2031Publisher
MIT PRESS
DOI: 10.1162/jocn_a_01544
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Funding
- Marie Sklodowska-Curie Individual Fellowship
- Sainsbury Wellcome Centre/Gatsby Computational Unit Research Fellowship
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Convolutional neural networks (CNNs) are successful tools inspired by early findings in biological vision research, serving as advanced models for neural activity and visual behavior. Experimenting with and understanding CNNs can provide deeper insights into biological vision, while also presenting new opportunities for their use in vision research.
Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNs in vision research beyond basic object recognition.
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