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Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible.
A logical calculus of the ideas immanent in nervous activity
Warren S McCulloch and Walter Pitts · 1943
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The recent excitement about neural networks
Francis Crick · 1989
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Contrastive hebbian learning in the continuous hopfield model
Javier R Movellan · 1991
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Backpropagation: The basic theory
David E Rumelhart, Richard Durbin, Richard Golden, and Yves Chauvin · 1995
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A view of the em algorithm that justifies incremental, sparse, and other variants
Radford M Neal and Geoffrey E Hinton · 1998
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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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Cubic regularization of newton method and its global performance
Yurii Nesterov and Boris T Polyak · 2006
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Hierarchical models in the brain
Karl Friston · 2008
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Proximal algorithms
Neal Parikh and Stephen Boyd · 2014
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Implicit stochastic gradient descent for principled estimation with large datasets
Panos Toulis and Edoardo M Airoldi · 2014
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How important is weight symmetry in backpropagation?
Qianli Liao, Joel Leibo, and Tomaso Poggio · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
Cited alongside, same era.
Stochastic gradient methods for principled estimation with large datasets., 2016
Panos Toulis and Edoardo M Airoldi · 2016
Cited alongside, same era.
Towards deep learning with segregated dendrites
Jordan Guerguiev, Timothy P Lillicrap, and Blake A Richards · 2017
Cited alongside, same era.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Benjamin Scellier and Yoshua Bengio · 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.
Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Tightening the biological constraints on gradient-based predictive coding
Nicholas Alonso and Emre Neftci · 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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Associative memories via predictive coding
Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder, Zhenghua Xu, Rafal Bogacz, and Thomas Lukasiewicz · 2021
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A theoretical framework for inference learning
Nick Alonso, Beren Millidge, Jeffrey Krichmar, and Emre O Neftci · 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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Sergey Bartunov, Adam Santoro, Blake A Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap · 2018
Cited alongside, same era.
Dendritic solutions to the credit assignment problem
Blake A Richards and Timothy P Lillicrap · 2019
Cited alongside, same era.
Theories of error back-propagation in the brain
James CR Whittington and Rafal Bogacz · 2019
Cited alongside, same era.
Backpropagation and the brain
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, and Geoffrey Hinton · 2020
Cited alongside, same era.
Predictive coding approximates backprop along arbitrary computation graphs
Beren Millidge, Alexander Tschantz, and Christopher L Buckley · 2020
Cited alongside, same era.
Can the brain do backpropagation? exact implementation of backpropagation in predictive coding networks
Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, and Rafal Bogacz · 2020
Cited alongside, same era.
Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, and Rafal Bogacz · 2022
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A theoretical framework for inference and learning in predictive coding networks
Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, and Rafal Bogacz · 2022
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On the relationship between predictive coding and backpropagation
Robert Rosenbaum · 2022
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Learning on arbitrary graph topologies via predictive coding
Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song, Rafal Bogacz, and Thomas Lukasiewicz · 2022
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Incremental predictive coding: A parallel and fully automatic learning algorithm
Tommaso Salvatori, Yuhang Song, Beren Millidge, Zhenghua Xu, Lei Sha, Cornelius Emde, Rafal Bogacz, and Thomas Lukasiewicz · 2022
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Inferring neural activity before plasticity: A foundation for learning beyond backpropagation
Yuhang Song, Beren Gray Millidge, Tommaso Salvatori, Thomas Lukasiewicz, Zhenghua Xu, and Rafal Bogacz · 2022
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