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

Published online by Cambridge University Press:  06 December 2023

Yaoda Xu Open the ORCID record for Yaoda Xu [Opens in a new window]  and
Maryam Vaziri-Pashkam
Yaoda Xu
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA yaoda.xu@yale.edu, https://sites.google.com/view/yaodaxu/home
Maryam Vaziri-Pashkam
Affiliation:
National Institute of Mental Health, Bethesda, MD, USA maryam.vaziri-pashkam@nih.gov, https://mvaziri.github.io/Homepage/Bio.html
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Abstract

Bowers 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.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e413
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Bakhtiari, S., Mineault, P., Lillicrap, T., Pack, C., & Richards, B. (2021). The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. Advances in Neural Information Processing Systems, 34, 25164–25178.Google Scholar
Blauch, N. M., Behrmann, M., & Plaut, D. C. (2022). A connectivity-constrained computational account of topographic organization in primate high-level visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 119, e2112566119.CrossRefGoogle ScholarPubMed
DiCarlo, J. J., & Cox, D. D. (2007). Untangling invariant object recognition. Trends in Cognitive Science, 11, 333–341.CrossRefGoogle ScholarPubMed
DiCarlo, J. J., Zoccolan, D., & Rust, R. C. (2012). How does the brain solve visual object recognition? Neuron, 73, 415–434.CrossRefGoogle ScholarPubMed
Jeong, S. K., & Xu, Y. (2017). Task-context dependent linear representation of multiple visual objects in human parietal cortex. Journal of Cognitive Neuroscience, 29, 1778–1789.CrossRefGoogle ScholarPubMed
Kanwisher, N., Khosla, M., & Dobs, K. (2023). Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neuroscience, 46, 240–254.CrossRefGoogle ScholarPubMed
Kay, K. N. (2018). Principles for models of neural information processing. NeuroImage, 180, 101–109.CrossRefGoogle ScholarPubMed
Mocz, V., Jeong, S. K., Chun, M., & Xu, Y. (2023). The representation of multiple visual objects in human ventral visual areas and in convolutional neural networks. Scientific Reports, 13, 9088.CrossRefGoogle Scholar
Serre, T. (2019). Deep learning: the good, the bad, and the ugly. Annual Review of Vision Science, 5, 399–426.CrossRefGoogle ScholarPubMed
Tacchetti, A., Isik, L., & Poggio, T. A. (2018). Invariant recognition shapes neural representations of visual input. Annual Review of Vision Science, 4, 403–422.CrossRefGoogle ScholarPubMed
Tang, K., Chin, M., Chun, M., & Xu, Y. (2022). The contribution of object identity and configuration to scene representation in convolutional neural networks. PLoS ONE, 17, e0270667.CrossRefGoogle ScholarPubMed
Taylor, J., & Xu, Y. (2021). Joint representation of color and shape in convolutional neural networks: A stimulus-rich network perspective. PLoS ONE, 16, e0253442.CrossRefGoogle Scholar
Vaziri-Pashkam, M., Taylor, J., & Xu, Y. (2019). Spatial frequency tolerant visual object representations in the human ventral and dorsal visual processing pathways. Journal of Cognitive Neuroscience, 31, 49–63.CrossRefGoogle ScholarPubMed
Vaziri-Pashkam, M., & Xu, Y. (2019). An information-driven 2-pathway characterization of occipitotemporal and posterior parietal visual object representations. Cerebral Cortex, 29, 2034–2050.CrossRefGoogle ScholarPubMed
Xu, Y., & Vaziri-Pashkam, M. (2021a). Limited correspondence in visual representation between the human brain and convolutional neural networks. Nature Communications, 12, 2065.CrossRefGoogle ScholarPubMed
Xu, Y., & Vaziri-Pashkam, M. (2021b). The coding of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks. Journal of Neuroscience, 41, 4234–4252.CrossRefGoogle ScholarPubMed
Xu, Y., & Vaziri-Pashkam, M. (2022). Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks. NeuroImage, 263, 119635.CrossRefGoogle ScholarPubMed