Using DNNs to understand the primate vision: A shortcut or a distraction?

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dc.contributor.authorXu, Yaoda
dc.contributor.authorVaziri-Pashkam, Maryam
dc.date.accessioned2025-02-20T15:41:42Z
dc.date.available2025-02-20T15:41:42Z
dc.date.issued2023-12-06
dc.descriptionThis article was originally published in Behavioral and Brain Sciences. The version of record is available at: https://doi.org/10.1017/S0140525X23001528 Copyright © The Author(s), 2023. Published by Cambridge University Press. This article will be embargoed until 06/06/2024.
dc.description.abstractBowers et al. bring forward critical issues in the current use of deep neural networks (DNNs) to model primate vision. Our own research further reveals fundamentally different algorithms utilized by DNNs for visual processing compared to the brain. It is time to reemphasize the value of basic vision research and put more resources and effort on understanding the primate brain itself.
dc.description.sponsorshipY. X. was supported by the National Institute of Health (NIH) Grant 1R01EY030854. M. V.-P. was supported by NIH Intramural Research Program ZIA MH002035.
dc.identifier.citationXu, Yaoda, and Maryam Vaziri-Pashkam. “Using DNNs to Understand the Primate Vision: A Shortcut or a Distraction?” Behavioral and Brain Sciences 46 (2023): e413. https://doi.org/10.1017/S0140525X23001528.
dc.identifier.issn1469-1825
dc.identifier.urihttps://udspace.udel.edu/handle/19716/35825
dc.language.isoen_US
dc.publisherBehavioral and Brain Sciences
dc.titleUsing DNNs to understand the primate vision: A shortcut or a distraction?
dc.typeOpen Peer Commentary

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