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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:

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
Dharshan Kumaran, Demis Hassabis, James L. McClelland
Trends in Cognitive Sciences (2016) Vol. 20, Iss. 7, pp. 512-534
Closed Access | Times Cited: 595

Showing 1-25 of 595 citing articles:

Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, et al.
Proceedings of the National Academy of Sciences (2017) Vol. 114, Iss. 13, pp. 3521-3526
Open Access | Times Cited: 5292
Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, et al.
Neural Networks (2019) Vol. 113, pp. 54-71
Open Access | Times Cited: 2461
Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, et al.
Nature (2016) Vol. 538, Iss. 7626, pp. 471-476
Closed Access | Times Cited: 1358
Neuroscience-Inspired Artificial Intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, et al.
Neuron (2017) Vol. 95, Iss. 2, pp. 245-258
Open Access | Times Cited: 1334
Mechanisms of systems memory consolidation during sleep
Jens G. Klinzing, Niels Niethard, Jan Born
Nature Neuroscience (2019) Vol. 22, Iss. 10, pp. 1598-1610
Closed Access | Times Cited: 973
Toward an Integration of Deep Learning and Neuroscience
Adam Marblestone, Greg Wayne, Konrad P. Körding
Frontiers in Computational Neuroscience (2016) Vol. 10
Open Access | Times Cited: 630
Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges
Jianshi Tang, Fang Yuan, Xinke Shen, et al.
Advanced Materials (2019) Vol. 31, Iss. 49
Closed Access | Times Cited: 611
Reinforcement Learning, Fast and Slow
Matthew Botvinick, Sam Ritter, Jane X. Wang, et al.
Trends in Cognitive Sciences (2019) Vol. 23, Iss. 5, pp. 408-422
Open Access | Times Cited: 608
Navigating cognition: Spatial codes for human thinking
Jacob L. S. Bellmund, Peter Gärdenfors, Edvard I Moser, et al.
Science (2018) Vol. 362, Iss. 6415
Open Access | Times Cited: 556
Neural scene representation and rendering
S. M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, et al.
Science (2018) Vol. 360, Iss. 6394, pp. 1204-1210
Closed Access | Times Cited: 532
Three scenarios for continual learning
Gido M. van de Ven, Andreas S. Tolias
arXiv (Cornell University) (2019)
Open Access | Times Cited: 492
Cognitive computational neuroscience
Nikolaus Kriegeskorte, Pamela K. Douglas
Nature Neuroscience (2018) Vol. 21, Iss. 9, pp. 1148-1160
Open Access | Times Cited: 468
2022 roadmap on neuromorphic computing and engineering
Dennis Valbjørn Christensen, Regina Dittmann, B. Linares-Barranco, et al.
Neuromorphic Computing and Engineering (2022) Vol. 2, Iss. 2, pp. 022501-022501
Open Access | Times Cited: 459
The hippocampal sharp wave–ripple in memory retrieval for immediate use and consolidation
Hannah R. Joo, Loren M. Frank
Nature reviews. Neuroscience (2018) Vol. 19, Iss. 12, pp. 744-757
Open Access | Times Cited: 378
Learning to Prompt for Continual Learning
Zifeng Wang, Zizhao Zhang, Chen‐Yu Lee, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 139-149
Open Access | Times Cited: 341
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: 340
Brain-inspired replay for continual learning with artificial neural networks
Gido M. van de Ven, Hava T. Siegelmann, Andreas S. Tolias
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 335
The hippocampus and related neocortical structures in memory transformation
Melanie J. Sekeres, Gordon Winocur, Morris Moscovitch
Neuroscience Letters (2018) Vol. 680, pp. 39-53
Closed Access | Times Cited: 334
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks
Uri Hasson, Samuel A. Nastase, Ariel Goldstein
Neuron (2020) Vol. 105, Iss. 3, pp. 416-434
Open Access | Times Cited: 303
The Persistence and Transience of Memory
Blake A. Richards, Paul W. Frankland
Neuron (2017) Vol. 94, Iss. 6, pp. 1071-1084
Open Access | Times Cited: 282
Reinforcement learning in artificial and biological systems
Emre Neftci, Bruno B. Averbeck
Nature Machine Intelligence (2019) Vol. 1, Iss. 3, pp. 133-143
Closed Access | Times Cited: 250
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
Emre Neftci, Charles Augustine, Somnath Paul, et al.
Frontiers in Neuroscience (2017) Vol. 11
Open Access | Times Cited: 218
Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, et al.
Nature Machine Intelligence (2019) Vol. 1, Iss. 8, pp. 364-372
Closed Access | Times Cited: 216
Deep Reinforcement Learning and Its Neuroscientific Implications
Matthew Botvinick, Jane X. Wang, Will Dabney, et al.
Neuron (2020) Vol. 107, Iss. 4, pp. 603-616
Open Access | Times Cited: 204
Adversarial Reciprocal Points Learning for Open Set Recognition
Guangyao Chen, Peixi Peng, Xiangqian Wang, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021), pp. 1-1
Open Access | Times Cited: 189
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