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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:

Letter perception emerges from unsupervised deep learning and recycling of natural image features
Alberto Testolin, Ivilin Stoianov, Marco Zorzi
Nature Human Behaviour (2017) Vol. 1, Iss. 9, pp. 657-664
Closed Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

The secret life of predictive brains: what’s spontaneous activity for?
Giovanni Pezzulo, Marco Zorzi, Maurizio Corbetta
Trends in Cognitive Sciences (2021) Vol. 25, Iss. 9, pp. 730-743
Open Access | Times Cited: 134
A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
Jianjun Ni, Yinan Chen, Yan Chen, et al.
Applied Sciences (2020) Vol. 10, Iss. 8, pp. 2749-2749
Open Access | Times Cited: 133
A systematic review on deep learning architectures and applications
Aditya Khamparia, Karan Mehtab Singh
Expert Systems (2019) Vol. 36, Iss. 3
Closed Access | Times Cited: 108
A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images
Sucheta Chauhan, Lovekesh Vig, Michele De Filippo De Grazia, et al.
Frontiers in Neuroinformatics (2019) Vol. 13
Open Access | Times Cited: 76
Unsupervised learning predicts human perception and misperception of gloss
Katherine R. Storrs, Barton L. Anderson, Roland W. Fleming
Nature Human Behaviour (2021) Vol. 5, Iss. 10, pp. 1402-1417
Open Access | Times Cited: 60
An emergentist perspective on the origin of number sense
Marco Zorzi, Alberto Testolin
Philosophical Transactions of the Royal Society B Biological Sciences (2018) Vol. 373, Iss. 1740, pp. 20170043-20170043
Open Access | Times Cited: 72
The evolution of early symbolic behavior inHomo sapiens
Kristian Tylén, Riccardo Fusaroli, Sergio Rojo, et al.
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 9, pp. 4578-4584
Open Access | Times Cited: 63
Graphic complexity in writing systems
Helena Miton, Olivier Morin
Cognition (2021) Vol. 214, pp. 104771-104771
Open Access | Times Cited: 41
Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions
Alberto Testolin, Marco Zorzi
Frontiers in Computational Neuroscience (2016) Vol. 10
Open Access | Times Cited: 54
Visual sense of number vs. sense of magnitude in humans and machines
Alberto Testolin, Serena Dolfi, Mathijs Rochus, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 48
Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading
Thomas Hannagan, Aakash Agrawal, Laurent Cohen, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 46
Open Access | Times Cited: 36
Deep learning—Using machine learning to study biological vision
Najib J. Majaj, Denis G. Pelli
Journal of Vision (2018) Vol. 18, Iss. 13, pp. 2-2
Open Access | Times Cited: 40
Investigating the intrinsic top-down dynamics of deep generative models
Lorenzo Tausani, Alberto Testolin, Marco Zorzi
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition
Marijn van Vliet, Oona Rinkinen, Takao Shimizu, et al.
(2025)
Open Access
Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets
Alberto Testolin, Roee Diamant
Sensors (2020) Vol. 20, Iss. 10, pp. 2945-2945
Open Access | Times Cited: 29
A Developmental Approach for Training Deep Belief Networks
Matteo Zambra, Alberto Testolin, Marco Zorzi
Cognitive Computation (2022) Vol. 15, Iss. 1, pp. 103-120
Open Access | Times Cited: 17
Unsupervised learning reveals interpretable latent representations for translucency perception
Chenxi Liao, Masataka Sawayama, Bei Xiao
PLoS Computational Biology (2023) Vol. 19, Iss. 2, pp. e1010878-e1010878
Open Access | Times Cited: 9
Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity
Veronica Montani, Valérie Chanoine, Ivilin Stoianov, et al.
Brain and Language (2019) Vol. 192, pp. 1-14
Open Access | Times Cited: 23
Deep learning systems as complex networks
Alberto Testolin, Michele Piccolini, Samir Suweis
Journal of Complex Networks (2019)
Open Access | Times Cited: 23
Enhancing the Utility of Preclinical Research in Neuropsychiatry Drug Development
Arie Kaffman, Jordon D. White, Lan Wei, et al.
Methods in molecular biology (2019), pp. 3-22
Open Access | Times Cited: 22
Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?
Andrea De Cesarei, Shari Cavicchi, Giampaolo Cristadoro, et al.
Cognitive Science (2021) Vol. 45, Iss. 6
Open Access | Times Cited: 16
The Challenge of Modeling the Acquisition of Mathematical Concepts
Alberto Testolin
Frontiers in Human Neuroscience (2020) Vol. 14
Open Access | Times Cited: 16
The secret life of predictive brains: what's spontaneous activity for?
Giovanni Pezzulo, Marco Zorzi, Maurizio Corbetta
(2020)
Closed Access | Times Cited: 15
Where is mirror invariance? Masked priming effects by mirrored and rotated transformations of reversible and nonreversible letters
Tânia Fernandes, Eduardo Xavier, Miguel Domingues, et al.
Journal of Memory and Language (2022) Vol. 127, pp. 104375-104375
Closed Access | Times Cited: 9
General object-based features account for letter perception
Daniel Janini, Chris Hamblin, Arturo Deza, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 9, pp. e1010522-e1010522
Open Access | Times Cited: 9
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