Fetching the paper…
Reading the bibliography…
The backpropagation of error algorithm (BP) is impossible to implement in a real brain.
A learning algorithm for boltzmann machines
Ackley, David H, Hinton, Geoffrey E, and Sejnowski, Terrence J · 1985
Earlier work this paper cites.
Learning process in an asymmetric threshold network
LeCun, Yann · 1986
Earlier work this paper cites.
Learning representations by back-propagation errors
Rumelhart, DE, Hinton, GE, and Williams, RJ · 1986
Earlier work this paper cites.
Competitive learning: From interactive activation to adaptive resonance
Grossberg, Stephen · 1987
Earlier work this paper cites.
Modèles connexionnistes de l’apprentissage
LeCun, Yann · 1987
Earlier work this paper cites.
Generalization of back-propagation to recurrent neural networks
Pineda, Fernando J · 1987
Earlier work this paper cites.
Learning representations by recirculation
Hinton, Geoffrey E and McClelland, James L · 1988
Earlier work this paper cites.
Dynamics and architecture for neural computation
Pineda, Fernando J · 1988
Earlier work this paper cites.
The recent excitement about neural networks
Crick, Francis · 1989
Earlier work this paper cites.
A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
Almeida, Luis B · 1990
Earlier work this paper cites.
Contrastive hebbian learning in the continuous hopfield model
Movellan, Javier R · 1991
Earlier work this paper cites.
Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
O’Reilly, Randall C · 1996
Earlier work this paper cites.
Supervised and unsupervised learning with two sites of synaptic integration
Körding, Konrad P and König, Peter · 2001
Earlier work this paper cites.
Equivalence of backpropagation and contrastive hebbian learning in a layered network
Xie, Xiaohui and Seung, H Sebastian · 2003
Cited alongside, same era.
How to do backpropagation in a brain
Hinton, G.E · 2007
Cited alongside, same era.
Solving the problem of negative synaptic weights in cortical models
Parisien, Christopher, Anderson, Charles H, and Eliasmith, Chris · 2008
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, Alex · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
Later among the works it cites.
Feedforward initialization for fast inference of deep generative networks is biologically plausible
Bengio, Yoshua, Scellier, Benjamin, Bilaniuk, Olexa, Sacramento, Joao, and Senn, Walter · 2016
Later among the works it cites.
A guide to convolution arithmetic for deep learning
Dumoulin, Vincent and Visin, Francesco · 2016
Later among the works it cites.
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, Timothy P, Cownden, Daniel, Tweed, Douglas B, and Akerman, Colin J · 2016
Later among the works it cites.
Direct feedback alignment provides learning in deep neural networks
Nøkland, Arild · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
How auto-encoders could provide credit assignment in deep networks via target propagation
Bengio, Yoshua · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
Cited alongside, same era.
Random feedback weights support learning in deep neural networks
Lillicrap, Timothy P, Cownden, Daniel, Tweed, Douglas B, and Akerman, Colin J · 2014
Cited alongside, same era.
Striving for simplicity: The all convolutional net
Springenberg, Jost Tobias, Dosovitskiy, Alexey, Brox, Thomas, and Riedmiller, Martin · 2014
Cited alongside, same era.
Early inference in energy-based models approximates back-propagation
Bengio, Yoshua and Fischer, Asja · 2015
Cited alongside, same era.
Towards biologically plausible deep learning
Bengio, Yoshua, Lee, Dong-Hyun, Bornschein, Jorg, Mesnard, Thomas, and Lin, Zhouhan · 2015
Cited alongside, same era.
Later among the works it cites.
Stdp-compatible approximation of backpropagation in an energy-based model
Bengio, Yoshua, Mesnard, Thomas, Fischer, Asja, Zhang, Saizheng, and Wu, Yuhuai · 2017
Later among the works it cites.
Towards deep learning with segregated dendrites
Guerguiev, Jordan, Lillicrap, Timothy P, and Richards, Blake A · 2017
Later among the works it cites.
Dendritic error backpropagation in deep cortical microcircuits
Sacramento, Joao, Costa, Rui Ponte, Bengio, Yoshua, and Senn, Walter · 2017
Later among the works it cites.
Deep learning with dynamic spiking neurons and fixed feedback weights
Samadi, Arash, Lillicrap, Timothy P, and Tweed, Douglas B · 2017
Later among the works it cites.
Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Scellier, Benjamin and Bengio, Yoshua · 2017
Later among the works it cites.
An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
Whittington, James CR and Bogacz, Rafal · 2017
Later among the works it cites.
Biologically motivated algorithms for propagating local target representations
Ororbia, Alexander G and Mali, Ankur · 2018
Closest in time.
Conducting credit assignment by aligning local representations
Ororbia, Alexander G, Mali, Ankur, Kifer, Daniel, and Giles, C Lee · 2018
Closest in time.