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Equilibrium propagation (EP) is an alternative to backpropagation (BP) that allows the training of deep neural networks with local learning rules.
Neurons with graded response have collective computational properties like those of two-state neurons
John J Hopfield · 1984
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Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Parabolic bursting in an excitable system coupled with a slow oscillation
G Bard Ermentrout and Nancy Kopell · 1986
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Neuromorphic electronic systems
Carver Mead · 1990
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Contrastive learning and neural oscillations
Pierre Baldi and Fernando Pineda · 1991
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Contrastive hebbian learning in the continuous hopfield model
Javier R Movellan · 1991
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Dynamics of fully complex-valued neural networks
Akira Hirose · 1992
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Complex-valued multistate neural associative memory
Stanislaw Jankowski, Andrzej Lozowski, and Jacek M Zurada · 1996
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh PN Rao and Dana H Ballard · 1999
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Dynamic predictions: oscillations and synchrony in top–down processing
Andreas K Engel, Pascal Fries, and Wolf Singer · 2001
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Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J Sejnowski · 2002
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Spike timing-dependent plasticity of neural circuits
Yang Dan and Mu-ming Poo · 2004
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The role of acetylcholine in learning and memory
M. Hasselmo · 2006
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Mathematics for physics and physicists
Walter Appel · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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The free-energy principle: a unified brain theory?
Karl Friston · 2010
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The role of phase synchronization in memory processes
Juergen Fell and Nikolai Axmacher · 2011
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Predictive coding
Yanping Huang and Rajesh PN Rao · 2011
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Neuromorphic silicon neuron circuits
Giacomo Indiveri, Bernabé Linares-Barranco, Tara Julia Hamilton, André van Schaik, Ralph Etienne-Cummings, Tobi Delbruck, Shih-Chii Liu, Piotr Dudek, Philipp Häfliger, Sylvie Renaud, et al · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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How auto-encoders could provide credit assignment in deep networks via target propagation
Yoshua Bengio · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Difference target propagation
Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, and Yoshua Bengio · 2015
Cited alongside, same era.
Random synaptic feedback weights support error backpropagation for deep learning
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
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Direct feedback alignment provides learning in deep neural networks
Arild Nøkland · 2016
Cited alongside, same era.
Spintronic nanodevices for bioinspired computing
Julie Grollier, Damien Querlioz, and Mark D Stiles · 2016
Cited alongside, same era.
Toward on-chip acceleration of the backpropagation algorithm using nonvolatile memory
Pritish Narayanan, Alessandro Fumarola, Lucas L Sanches, Kohji Hosokawa, Scott C Lewis, Robert M Shelby, and Geoffrey W Burr · 2017
Cited alongside, same era.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Training end-to-end analog neural networks with equilibrium propagation
Jack Kendall, Ross Pantone, Kalpana Manickavasagam, Yoshua Bengio, and Benjamin Scellier · 2020
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Equilibrium propagation for memristor-based recurrent neural networks
Gianluca Zoppo, Francesco Marrone, and Fernando Corinto · 2020
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Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
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A biologically plausible neural network for slow feature analysis
David Lipshutz, Charles Windolf, Siavash Golkar, and Dmitri Chklovskii · 2020
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Predictive coding approximates backprop along arbitrary computation graphs
Beren Millidge, Alexander Tschantz, and Christopher L Buckley · 2020
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Benjamin Scellier and Yoshua Bengio · 2017
Cited alongside, same era.
A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Cited alongside, same era.
An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
James CR Whittington and Rafal Bogacz · 2017
Cited alongside, same era.
A survey of neuromorphic computing and neural networks in hardware
Catherine D Schuman, Thomas E Potok, Robert M Patton, J Douglas Birdwell, Mark E Dean, Garrett S Rose, and James S Plank · 2017
Cited alongside, same era.
Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap · 2018
Cited alongside, same era.
Equivalent-accuracy accelerated neural-network training using analogue memory
Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Robert M Shelby, Irem Boybat, Carmelo Di Nolfo, Severin Sidler, Massimo Giordano, Martina Bodini, Nathan CP Farinha, et al · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Gait-prop: A biologically plausible learning rule derived from backpropagation of error
Nasir Ahmad, Marcel A van Gerven, and Luca Ambrogioni · 2020
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Biological credit assignment through dynamic inversion of feedforward networks
Bill Podlaski and Christian K Machens · 2020
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Deriving differential target propagation from iterating approximate inverses
Yoshua Bengio · 2020
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A theoretical framework for target propagation
Alexander Meulemans, Francesco Carzaniga, Johan Suykens, João Sacramento, and Benjamin F Grewe · 2020
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Direct feedback alignment scales to modern deep learning tasks and architectures
Julien Launay, Iacopo Poli, François Boniface, and Florent Krzakala · 2020
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A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, and Wolfgang Maass · 2020
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Synaptic plasticity dynamics for deep continuous local learning (decolle)
Jacques Kaiser, Hesham Mostafa, and Emre Neftci · 2020
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A contrastive rule for meta-learning
Nicolas Zucchet, Simon Schug, Johannes von Oswald, Dominic Zhao, and João Sacramento · 2021
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Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, and Damien Querlioz · 2021
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Refining activation downsampling with softpool. arxiv 2021
A Stergiou, R Poppe, and G Kalliatakis · 2021
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Supervised learning in physical networks: From machine learning to learning machines
Menachem Stern, Daniel Hexner, Jason W Rocks, and Andrea J Liu · 2021
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A Richards, and Richard Naud · 2021
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Latent equilibrium: Arbitrarily fast computation with arbitrarily slow neurons
Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, and Mihai A Petrovici · 2021
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Eqspike: spike-driven equilibrium propagation for neuromorphic implementations
Erwann Martin, Maxence Ernoult, Jérémie Laydevant, Shuai Li, Damien Querlioz, Teodora Petrisor, and Julie Grollier · 2021
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A gradient estimator for time-varying electrical networks with non-linear dissipation
Jack Kendall · 2021
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Neurons learn by predicting future activity
Artur Luczak, Bruce L McNaughton, and Yoshimasa Kubo · 2022
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The combination of hebbian and predictive plasticity learns invariant object representations in deep sensory networks
Manu Srinath Halvagal and Friedemann Zenke · 2022
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On the relationship between predictive coding and backpropagation
Robert Rosenbaum · 2022
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Towards scaling difference target propagation by learning backprop targets
Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, and Yoshua Bengio · 2022
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Deep learning in spiking phasor neural networks
Connor Bybee, E Paxon Frady, and Friedrich T Sommer · 2022
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