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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Limited correspondence in visual representation between the human brain and convolutional neural networks
Yaoda Xu, Maryam Vaziri-Pashkam
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 13

Showing 13 citing articles:

Visual Imagery and Perception Share Neural Representations in the Alpha Frequency Band
Siying Xie, Daniel Kaiser, Radoslaw M. Cichy
Current Biology (2020) Vol. 30, Iss. 13, pp. 2621-2627.e5
Open Access | Times Cited: 120
Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects
Timothy F. Brady, Viola S. Störmer
Memory & Cognition (2023) Vol. 52, Iss. 3, pp. 595-609
Closed Access | Times Cited: 20
What shapes feature representations? Exploring datasets, architectures, and training
Katherine L. Hermann, Andrew K. Lampinen
arXiv (Cornell University) (2020)
Open Access | Times Cited: 47
Diverse deep neural networks all predict human IT well, after training and fitting
Katherine R. Storrs, Tim C. Kietzmann, Alexander Walther, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 44
Using deep neural networks to evaluate object vision tasks in rats
Kasper Vinken, Hans Op de Beeck
PLoS Computational Biology (2021) Vol. 17, Iss. 3, pp. e1008714-e1008714
Open Access | Times Cited: 20
Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects
Timothy F. Brady, Viola S. Störmer
(2020)
Open Access | Times Cited: 12
Untangling the animacy organization of occipitotemporal cortex
J. Brendan Ritchie, Astrid Zeman, Joyce Bosmans, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 4
Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks
Yaoda Xu, Maryam Vaziri-Pashkam
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 2
General object-based features account for letter perception
Daniel Janini, Chris Hamblin, Arturo Deza, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 2
Learning compact generalizable neural representations supporting perceptual grouping
Vijay Veerabadran, Virginia R. de
arXiv (Cornell University) (2020)
Open Access | Times Cited: 1
Seeing Eye-to-Eye? A Comparison of Object Recognition Performance in Humans and Deep Convolutional Neural Networks under Image Manipulation.
Leonard E. van Dyck, Walter Gruber
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 1
The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks
Yaoda Xu, Maryam Vaziri-Pashkam
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 1
Two distinct networks containing position-tolerant representations of actions in the human brain
Elahe’ Yargholi, Gholam‐Ali Hossein‐Zadeh, Maryam Vaziri-Pashkam
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 1
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