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  Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Golan, T., Taylor, J., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., et al. (2023). Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses. Behavioral and Brain Sciences, 46: e392. doi:10.1017/S0140525X23001553.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000E-05F3-A Versions-Permalink: https://hdl.handle.net/21.11116/0000-000F-19E4-4
Genre: Zeitschriftenartikel

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Golan_2023.pdf (beliebiger Volltext), 201KB
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https://hdl.handle.net/21.11116/0000-000F-19E5-3
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Golan_2023.pdf
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Urheber

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 Urheber:
Golan, Tal1, Autor
Taylor, JohnMark2, Autor
Schütt, Heiko2, 3, Autor
Peters, Benjamin4, Autor
Sommers, Rowan P.5, Autor
Seeliger, Katja6, Autor           
Doerig, Adrien7, Autor
Linton, Paul2, 8, 9, Autor
Konkle, Talia10, Autor
van Gerven, Marcel11, Autor
Kording, Konrad12, 13, Autor
Richards, Blake13, 14, 15, 16, 17, Autor
Kietzmann, Tim C.7, Autor
Lindsay, Grace W.18, Autor
Kriegeskorte, Nikolaus2, 19, Autor
Affiliations:
1School of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel, ou_persistent22              
2Zuckerman Mind Brain Behavior Institute, Columbia University in the City of New York, NY, USA, ou_persistent22              
3Center for Neural Science, New York University, NY, USA, ou_persistent22              
4School of Psychology and Neuroscience, University of Glasgow, United Kingdom, ou_persistent22              
5Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands, ou_persistent22              
6Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3158378              
7Institute of Cognitive Science, University of Osnabrück, Germany, ou_persistent22              
8Presidential Scholars in Society and Neuroscience, Center for Science and Society, Columbia University in the City of New York, NY, USA, ou_persistent22              
9Italian Academy for Advanced Studies in America, Columbia University in the City of New York, NY, USA, ou_persistent22              
10Department of Psychology, Harvard University, Cambridge, MA, USA, ou_persistent22              
11Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands, ou_persistent22              
12Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA, ou_persistent22              
13Learning in Machines and Brains Program, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada, ou_persistent22              
14Mila – Quebec Artificial Intelligence Institute, Montréal, QC, Canada, ou_persistent22              
15School of Computer Science, McGill University, Montréal, QC, Canada, ou_persistent22              
16Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, QC, Canada, ou_persistent22              
17Montreal Neurological Institute and Hospital, McGill University, QC, Canada, ou_persistent22              
18Data Science Institute, Columbia University in the City of New York, NY, USA, ou_persistent22              
19Department of Psychology, Neuroscience, and Electrical Engineering, Columbia University in the City of New York, NY, USA, ou_persistent22              

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Schlagwörter: -
 Zusammenfassung: An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

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Sprache(n): eng - English
 Datum: 2023-12-06
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1017/S0140525X23001553
PMID: 38054329
 Art des Abschluß: -

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Quelle 1

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Titel: Behavioral and Brain Sciences
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: New York : Cambridge University Press.
Seiten: - Band / Heft: 46 Artikelnummer: e392 Start- / Endseite: - Identifikator: ISSN: 0140-525X
CoNE: https://pure.mpg.de/cone/journals/resource/954925341730