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The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.
Neurons with graded response have collective computational properties like those of two-state neurons
J. J. Hopfield · 1984
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A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
L. B. Almeida · 1987
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Generalization of back-propagation to recurrent neural networks
F. J. Pineda · 1987
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Learning representations by recirculation
G. E. Hinton and J. L. McClelland · 1988
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The recent excitement about neural networks
F. Crick · 1989
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Action potentials propagating back into dendrites triggers changes in efficacy
H. Markram and B. Sakmann · 1995
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A neuronal learning rule for sub-millisecond temporal coding
W. Gerstner, R. Kempter, J. L. van Hemmen, and H. Wagner · 1996
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Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
R. C. O’Reilly · 1996
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Nonlinear backpropagation: doing backpropagation without derivatives of the activation function
J. Hertz, A. Krogh, B. Lautrup, and T. Lehmann · 1997
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Synaptic modification by correlated activity: Hebb’s postulate revisited
G.-q. Bi and M.-m. Poo · 2001
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Theoretical Neuroscience
P. Dayan and L. F. Abbott · 2001
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Voltage and spike timing interact in stdp–a unified model
C. Clopath and W. Gerstner · 2010
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Questions about stdp as a general model of synaptic plasticity
J. Lisman and N. Spruston · 2010
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Timing is not everything: neuromodulation opens the stdp gate
V. Pawlak, J. R. Wickens, A. Kirkwood, and J. N. Kerr · 2010
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Spike-timing-dependent plasticity: a comprehensive overview
H. Markram, W. Gerstner, and P. J. Sjöström · 2012
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
N. Frémaux and W. Gerstner · 2016
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T. Mesnard, W. Gerstner, and J. Brea · 2016
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
B. Scellier and Y. Bengio
Cited in the paper.
Equivalence of equilibrium propagation and recurrent backpropagation
B. Scellier and Y. Bengio
Cited in the paper.
Y. Bengio, T. Mesnard, A. Fischer, S. Zhang, and Y. Wu · 2017
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Towards deep learning with segregated dendrites
J. Guerguiev, T. P. Lillicrap, and B. A. Richards · 2017
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Superspike: Supervised learning in multi-layer spiking neural networks
F. Zenke and S. Ganguli · 2017
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