Deep Neural Networks and Biological Vision Special Issue

Feature Editors

Nikolaus Kriegeskorte, Columbia University
Denis Pelli, New York University
Cristina Savin, New York University
Felix Wichmann, University of Tübingen

Articles will be added as they are published.

Which deep learning model can best explain object representations of within-category exemplars? Open Access
Dongha Lee
Journal of Vision September 2021, Vol.21, 12. https://doi.org/10.1167/jov.21.10.12
Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition Open Access
Connor J. Parde, Y. Ivette Colón, Matthew Q. Hill, Carlos D. Castillo, Prithviraj Dhar, Alice J. O’Toole
Journal of Vision August 2021, Vol. 21, 15. https://doi.org/10.1167/jov.21.8.15
Biased orientation representations can be explained by experience with nonuniform training set statistics Open Access
Margaret Henderson, John T. Serences
Journal of Vision August 2021, Vol. 21, 10. https://doi.org/10.1167/jov.21.8.10
Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model Open Access
Ross Goutcher, Christian Barrington, Paul B. Hibbard, Bruce Graham
Journal of Vision July 2021, Vol.21, 13. https://doi.org/10.1167/jov.21.7.13
A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes Open Access
Michele Svanera, Andrew T. Morgan, Lucy S. Petro, Lars Muckli
Journal of Vision July 2021, Vol.21, 5. https://doi.org/10.1167/jov.21.7.5
A deep-learning framework for human perception of abstract art composition Open Access
Pierre Lelièvre, Peter Neri
Journal of Vision May 2021, Vol.21, 9. https://doi.org/10.1167/jov.21.5.9
Facial expression is retained in deep networks trained for face identification Open Access
Y. Ivette Colón, Carlos D. Castillo, Alice J. O’Toole
Journal of Vision April 2021, Vol.21, 4. https://doi.org/10.1167/jov.21.4.4
Five points to check when comparing visual perception in humans and machines Open Access
Christina M. Funke, Judy Borowski, Karolina Stosio, Wieland Brendel, Thomas S. A. Wallis, Matthias Bethge
Journal of Vision March 2021, Vol.21, 16. https://doi.org/10.1167/jov.21.3.16
Exploring and explaining properties of motion processing in biological brains using a neural network Open Access
Reuben Rideaux, Andrew E. Welchman
Journal of Vision February 2021, Vol.21, 11. https://doi.org/10.1167/jov.21.2.11
The human visual system and CNNs can both support robust online translation tolerance following extreme displacements Open Access
Ryan Blything, Valerio Biscione, Ivan I. Vankov, Casimir J. H. Ludwig, Jeffrey S. Bowers
Journal of Vision February 2021, Vol.21, 9. https://doi.org/10.1167/jov.21.2.9
Selectivity and robustness of sparse coding networks Open Access
Dylan M. Paiton, Charles G. Frye, Sheng Y. Lundquist, Joel D. Bowen, Ryan Zarcone, Bruno A. Olshausen
Journal of Vision November 2020, Vol.20, 10. https://doi.org/10.1167/jov.20.12.10
A dual foveal-peripheral visual processing model implements efficient saccade selection Open Access
Emmanuel Daucé, Pierre Albiges, Laurent U. Perrinet
Journal of Vision August 2020, Vol.20, 22. https://doi.org/10.1167/jov.20.8.22
Deep learning—Using machine learning to study biological vision Open Access
Najib J. Majaj, Denis G. Pelli
Journal of Vision December 2018, Vol.18, 2. https://doi.org/10.1167/18.13.2

Deep Neural Networks and Biological Vision Special Issue

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