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  What comparing deep neural networks can teach us about human vision

Seeliger, K., & Hebart, M. N. (2024). What comparing deep neural networks can teach us about human vision. Nature Machine Intelligence, 6, 122-123. doi:10.1038/s42256-024-00789-8.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000E-7BC3-C Version Permalink: https://hdl.handle.net/21.11116/0000-000E-7BC4-B
Genre: Journal Article

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 Creators:
Seeliger, Katja1, Author           
Hebart, Martin N.1, Author                 
Affiliations:
1Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3158378              

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Free keywords: Computational neuroscience; Visual system
 Abstract: Recent work has demonstrated important parallels between human visual representations and those found in deep neural networks. A new study comparing functional MRI data to deep neural network models highlights factors that may determine this similarity.

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Language(s): eng - English
 Dates: 2024-02-062024-02
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s42256-024-00789-8
 Degree: -

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Title: Nature Machine Intelligence
Source Genre: Journal
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Publ. Info: London : Springer Nature Publishing
Pages: - Volume / Issue: 6 Sequence Number: - Start / End Page: 122 - 123 Identifier: ISSN: 2522-5839
CoNE: https://pure.mpg.de/cone/journals/resource/2522-5839