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In machine learning, error back-propagation in multi-layer neural networks (deep learning) has been impressively successful in supervised and reinforcement learning tasks.
Neural networks and physical systems with emergent collective computational abilities
John J Hopfield · 1982
Earlier work this paper cites.
Generalization of back-propagation to recurrent neural networks
Fernando J Pineda · 1987
Earlier work this paper cites.
Contrastive hebbian learning in the continuous hopfield model
Javier R Movellan · 1991
Earlier work this paper cites.
Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
Randall C O’Reilly · 1996
Earlier work this paper cites.
Nonlinear backpropagation: doing backpropagation without derivatives of the activation function
John Hertz, Anders Krogh, Benny Lautrup, and Torsten Lehmann · 1997
Earlier work this paper cites.
Hippocampal synaptic plasticity is modulated by theta rhythm in the fascia dentata of adult and aged freely behaving rats
G Orr, G Rao, FP Houston, BL McNaughton, and Carol A Barnes · 2001
Earlier work this paper cites.
Equivalence of backpropagation and contrastive hebbian learning in a layered network
Xiaohui Xie and H Sebastian Seung · 2003
Cited alongside, same era.
Timing is not everything: neuromodulation opens the stdp gate
Verena Pawlak, Jeffery R Wickens, Alfredo Kirkwood, and Jason ND Kerr · 2010
Cited alongside, same era.
Normalization as a canonical neural computation
Matteo Carandini and David J Heeger · 2012
Cited alongside, same era.
Learning by the dendritic prediction of somatic spiking
Robert Urbanczik and Walter Senn · 2014
Cited alongside, same era.
Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Nicolas Frémaux and Wulfram Gerstner · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Later among the works it cites.
Feedforward initialization for fast inference of deep generative networks is biologically plausible
Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, Joao Sacramento, and Walter Senn · 2016
Closest in time.
Deep learning with segregated dendrites
Jordan Guergiuev, Timothy P Lillicrap, and Blake A Richards · 2016
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Toward an integration of deep learning and neuroscience
Adam H Marblestone, Greg Wayne, and Konrad P Kording · 2016
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Towards a biologically plausible backprop
Benjamin Scellier and Yoshua Bengio · 2016
Closest in time.
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