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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:

Towards deep learning with segregated dendrites
Jordan Guerguiev, Timothy Lillicrap, Blake A. Richards
eLife (2017) Vol. 6
Open Access | Times Cited: 358

Showing 1-25 of 358 citing articles:

A deep learning framework for neuroscience
Blake A. Richards, Timothy Lillicrap, Philippe Beaudoin, et al.
Nature Neuroscience (2019) Vol. 22, Iss. 11, pp. 1761-1770
Open Access | Times Cited: 899
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks
Emre Neftci, Hesham Mostafa, Friedemann Zenke
IEEE Signal Processing Magazine (2019) Vol. 36, Iss. 6, pp. 51-63
Open Access | Times Cited: 868
Backpropagation and the brain
Timothy Lillicrap, Adam Santoro, Luke Marris, et al.
Nature reviews. Neuroscience (2020) Vol. 21, Iss. 6, pp. 335-346
Open Access | Times Cited: 708
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: 600
Deep Learning With Spiking Neurons: Opportunities and Challenges
Michael Pfeiffer, Thomas Pfeil
Frontiers in Neuroscience (2018) Vol. 12
Open Access | Times Cited: 597
Synaptic Plasticity Forms and Functions
Jeffrey C. Magee, Christine Grienberger
Annual Review of Neuroscience (2020) Vol. 43, Iss. 1, pp. 95-117
Closed Access | Times Cited: 572
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
Friedemann Zenke, Surya Ganguli
Neural Computation (2018) Vol. 30, Iss. 6, pp. 1514-1541
Open Access | Times Cited: 511
Theories of Error Back-Propagation in the Brain
James C. R. Whittington, Rafał Bogacz
Trends in Cognitive Sciences (2019) Vol. 23, Iss. 3, pp. 235-250
Open Access | Times Cited: 357
A review of learning in biologically plausible spiking neural networks
Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, et al.
Neural Networks (2019) Vol. 122, pp. 253-272
Open Access | Times Cited: 317
Enhanced Dendritic Compartmentalization in Human Cortical Neurons
Lou Beaulieu-Laroche, Enrique H.S. Toloza, Marie-Sophie van der Goes, et al.
Cell (2018) Vol. 175, Iss. 3, pp. 643-651.e14
Open Access | Times Cited: 281
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: 280
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules
Wulfram Gerstner, Marco P. Lehmann, Vasiliki Liakoni, et al.
Frontiers in Neural Circuits (2018) Vol. 12
Open Access | Times Cited: 280
Illuminating dendritic function with computational models
Panayiota Poirazi, Athanasia Papoutsi
Nature reviews. Neuroscience (2020) Vol. 21, Iss. 6, pp. 303-321
Closed Access | Times Cited: 243
Training Spiking Neural Networks Using Lessons From Deep Learning
Jason K. Eshraghian, Max Ward, Emre Neftci, et al.
Proceedings of the IEEE (2023) Vol. 111, Iss. 9, pp. 1016-1054
Open Access | Times Cited: 227
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, et al.
Nature Neuroscience (2021) Vol. 24, Iss. 7, pp. 1010-1019
Open Access | Times Cited: 194
Deep Neural Networks in Computational Neuroscience
Tim C. Kietzmann, Patrick McClure, Nikolaus Kriegeskorte
Oxford Research Encyclopedia of Neuroscience (2019)
Open Access | Times Cited: 175
Functional clustering of dendritic activity during decision-making
Aaron Kerlin, Boaz Mohar, Daniel Flickinger, et al.
eLife (2019) Vol. 8
Open Access | Times Cited: 158
Surrogate Gradient Learning in Spiking Neural Networks
Emre Neftci, Hesham Mostafa, Friedemann Zenke
arXiv (Cornell University) (2019)
Open Access | Times Cited: 146
Efficient Spike-Driven Learning With Dendritic Event-Based Processing
Shuangming Yang, Tian Gao, Jiang Wang, et al.
Frontiers in Neuroscience (2021) Vol. 15
Open Access | Times Cited: 133
Bidirectional synaptic plasticity rapidly modifies hippocampal representations
Aaron D. Milstein, Yiding Li, Katie C. Bittner, et al.
eLife (2021) Vol. 10
Open Access | Times Cited: 111
Neurons learn by predicting future activity
Artur Luczak, Bruce L. McNaughton, Yoshimasa Kubo
Nature Machine Intelligence (2022) Vol. 4, Iss. 1, pp. 62-72
Open Access | Times Cited: 72
Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs
Beren Millidge, Alexander Tschantz, Christopher L. Buckley
Neural Computation (2022) Vol. 34, Iss. 6, pp. 1329-1368
Open Access | Times Cited: 70
Are Dendrites Conceptually Useful?
Matthew E. Larkum
Neuroscience (2022) Vol. 489, pp. 4-14
Closed Access | Times Cited: 68
Interpretable and explainable machine learning: A methods‐centric overview with concrete examples
Ričards Marcinkevičs, Julia E. Vogt
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2023) Vol. 13, Iss. 3
Open Access | Times Cited: 65
Spike-driven multi-scale learning with hybrid mechanisms of spiking dendrites
Shuangming Yang, Yanwei Pang, Haowen Wang, et al.
Neurocomputing (2023) Vol. 542, pp. 126240-126240
Closed Access | Times Cited: 54
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