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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:

Recurrence is required to capture the representational dynamics of the human visual system
Tim C. Kietzmann, Courtney J. Spoerer, Lynn K. A. Sörensen, et al.
Proceedings of the National Academy of Sciences (2019) Vol. 116, Iss. 43, pp. 21854-21863
Open Access | Times Cited: 375

Showing 1-25 of 375 citing articles:

The neural architecture of language: Integrative modeling converges on predictive processing
Martin Schrimpf, Idan Blank, Greta Tuckute, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 45
Open Access | Times Cited: 363
If deep learning is the answer, what is the question?
Andrew Saxe, Stephanie Nelli, Christopher Summerfield
Nature reviews. Neuroscience (2020) Vol. 22, Iss. 1, pp. 55-67
Open Access | Times Cited: 342
Artificial Neural Networks for Neuroscientists: A Primer
Guangyu Robert Yang, Xiao‐Jing Wang
Neuron (2020) Vol. 107, Iss. 6, pp. 1048-1070
Open Access | Times Cited: 285
Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence
Martin Schrimpf, Jonas Kubilius, Michael J. Lee, et al.
Neuron (2020) Vol. 108, Iss. 3, pp. 413-423
Open Access | Times Cited: 178
Inhibitory stabilization and cortical computation
Sadra Sadeh, Claudia Clopath
Nature reviews. Neuroscience (2020) Vol. 22, Iss. 1, pp. 21-37
Closed Access | Times Cited: 162
Neural tuning and representational geometry
Nikolaus Kriegeskorte, Xue-Xin Wei
Nature reviews. Neuroscience (2021) Vol. 22, Iss. 11, pp. 703-718
Open Access | Times Cited: 144
The neuroconnectionist research programme
Adrien Doerig, Rowan P. Sommers, Katja Seeliger, et al.
Nature reviews. Neuroscience (2023) Vol. 24, Iss. 7, pp. 431-450
Open Access | Times Cited: 132
Limits to visual representational correspondence between convolutional neural networks and the human brain
Yaoda Xu, Maryam Vaziri-Pashkam
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 115
Biological constraints on neural network models of cognitive function
Friedemann Pulvermüller, Rosario Tomasello, Malte R. Henningsen‐Schomers, et al.
Nature reviews. Neuroscience (2021) Vol. 22, Iss. 8, pp. 488-502
Open Access | Times Cited: 113
Using artificial neural networks to ask ‘why’ questions of minds and brains
Nancy Kanwisher, Meenakshi Khosla, Katharina Dobs
Trends in Neurosciences (2023) Vol. 46, Iss. 3, pp. 240-254
Open Access | Times Cited: 112
An ecologically motivated image dataset for deep learning yields better models of human vision
Johannes Mehrer, Courtney J. Spoerer, Emer C. Jones, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 8
Open Access | Times Cited: 111
High-resolution image reconstruction with latent diffusion models from human brain activity
Yu Takagi, Shinji Nishimoto
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023), pp. 14453-14463
Open Access | Times Cited: 93
THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
Martin N. Hebart, Oliver Contier, Lina Teichmann, et al.
eLife (2023) Vol. 12
Open Access | Times Cited: 50
Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition
Kohitij Kar, James J. DiCarlo
Neuron (2020) Vol. 109, Iss. 1, pp. 164-176.e5
Open Access | Times Cited: 127
Individual differences among deep neural network models
Johannes Mehrer, Courtney J. Spoerer, Nikolaus Kriegeskorte, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 110
Qualitative similarities and differences in visual object representations between brains and deep networks
Georgin Jacob, R. T. Pramod, Harish Katti, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 102
Constructing and Forgetting Temporal Context in the Human Cerebral Cortex
Hsiang-Yun Sherry Chien, Christopher J. Honey
Neuron (2020) Vol. 106, Iss. 4, pp. 675-686.e11
Open Access | Times Cited: 101
Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision
Courtney J. Spoerer, Tim C. Kietzmann, Johannes Mehrer, et al.
PLoS Computational Biology (2020) Vol. 16, Iss. 10, pp. e1008215-e1008215
Open Access | Times Cited: 93
A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping
Jonathan A. Michaels, Stefan Schaffelhofer, Andres Agudelo-Toro, et al.
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 50, pp. 32124-32135
Open Access | Times Cited: 87
Going in circles is the way forward: the role of recurrence in visual inference
Ruben S. van Bergen, Nikolaus Kriegeskorte
Current Opinion in Neurobiology (2020) Vol. 65, pp. 176-193
Open Access | Times Cited: 84
Local features and global shape information in object classification by deep convolutional neural networks
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, et al.
Vision Research (2020) Vol. 172, pp. 46-61
Open Access | Times Cited: 78
The neural architecture of language: Integrative modeling converges on predictive processing
Martin Schrimpf, Idan Blank, Greta Tuckute, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 77
Diverse Deep Neural Networks All Predict Human Inferior Temporal Cortex Well, After Training and Fitting
Katherine R. Storrs, Tim C. Kietzmann, Alexander Walther, et al.
Journal of Cognitive Neuroscience (2021), pp. 1-21
Open Access | Times Cited: 77
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states
David Sabbagh, Pierre Ablin, Gaël Varoquaux, et al.
NeuroImage (2020) Vol. 222, pp. 116893-116893
Open Access | Times Cited: 75
GLiT: Neural Architecture Search for Global and Local Image Transformer
Boyu Chen, Peixia Li, Chuming Li, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
Open Access | Times Cited: 71
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