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We introduce Equilibrium Propagation, a learning framework for energy-based models.
Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex
Bienenstock, E. L., Cooper, L. N., and Munro, P. W. (1982) · 1982
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Neurons with graded responses have collective computational properties like those of two-state neurons
Hopfield, J. J. (1984) · 1984
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Learning and releaming in boltzmann machines
Hinton, G. E. and Sejnowski, T. J. (1986) · 1986
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A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
Almeida, L. B. (1987) · 1987
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Generalization of back-propagation to recurrent neural networks
Pineda, F. J. (1987) · 1987
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Contrastive Hebbian learning in the continuous Hopfield model
Movellan, J. R. (1990) · 1990
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Objective function formulation of the BCM theory of visual cortical plasticity: statistical connections, stability conditions
Intrator, N. and Cooper, L. N. (1992) · 1992
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Action potentials propagating back into dendrites triggers changes in efficacy
Markram, H. and Sakmann, B. (1995) · 1995
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A neuronal learning rule for sub-millisecond temporal coding
Gerstner, W., Kempter, R., van Hemmen, J., and Wagner, H. (1996) · 1996
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Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
O’Reilly, R. C. (1996) · 1996
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Nonlinear backpropagation: doing backpropagation without derivatives of the activation function
Hertz, J. A., Krogh, A., Lautrup, B., and Lehmann, T. (1997) · 1997
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The mnist database of handwritten digits
LeCun, Y. and Cortes, C. (1998) · 1998
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Spike-based learning rules and stabilization of persistent neural activity
Xie, X. and Seung, H. S. (2000) · 2000
Cited alongside, same era.
Synaptic modification by correlated activity: Hebb’s postulate revisited
Bi, G. and Poo, M. (2001) · 2001
Cited alongside, same era.
Spike-timing-dependent synaptic modification induced by natural spike trains
Froemke, R. C. and Dan, Y. (2002) · 2002
Cited alongside, same era.
Training products of experts by minimizing contrastive divergence
Hinton, G. E. (2002) · 2002
Cited alongside, same era.
Equivalence of backpropagation and contrastive Hebbian learning in a layered network
Xie, X. and Seung, H. S. (2003) · 2003
Cited alongside, same era.
Free-energy and the brain
Friston, K. J. and Stephan, K. E. (2007) · 2007
Cited alongside, same era.
A triplet spike-timing–dependent plasticity model generalizes the bienenstock–cooper–munro rule to higher-order spatiotemporal correlations
Gjorgjievaa, J., Clopathb, C., Audetc, J., and Pfister, J.-P. (2011) · 2011
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The spike timing dependence of plasticity
Feldman, D. E. (2012) · 2012
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Spike-timing-dependent plasticity: A comprehensive overview
Markram, H., Gerstner, W., and Sjöström, P. (2012) · 2012
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Random feedback weights support learning in deep neural networks
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J. (2014) · 2014
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Why are deep nets reversible: a simple theory, with implications for training
Arora, S., Liang, Y., and Ma, T. (2015) · 2015
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tieleman, T. (2008) · 2008
Cited alongside, same era.
Deep Boltzmann machines
Salakhutdinov, R. and Hinton, G. E. (2009) · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Cited alongside, same era.
On the Convergence Properties of Contrastive Divergence
Sutskever, I. and Tieleman, T. (2010) · 2010
Cited alongside, same era.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
Cited alongside, same era.
Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
Berkes, P., Orban, G., Lengyel, M., and Fiser, J. (2011) · 2011
Cited alongside, same era.
Early inference in energy-based models approximates back-propagation
Bengio, Y. and Fischer, A. (2015) · 2015
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Training recurrent networks online without backtracking
Ollivier, Y., Tallec, C., and Charpiat, G. (2015) · 2015
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Feedforward initialization for fast inference of deep generative networks is biologically plausible
Bengio, Y., Scellier, B., Bilaniuk, O., Sacramento, J., and Senn, W. (2016) · 2016
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Mesnard, T., Gerstner, W., and Brea, J. (2016) · 2016
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STDP as presynaptic activity times rate of change of postsynaptic activity approximates back-propagation
Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., and Wu, Y. (2017) · 2017
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Unbiased online recurrent optimization
Tallec, C. and Ollivier, Y. (2017) · 2017
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