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Ongoing studies have identified similarities between neural representations in biological networks and in deep artificial neural networks.
Learning process in an asymmetric threshold network
Y. Le Cun · 1986
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Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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Competitive learning: From interactive activation to adaptive resonance
S. Grossberg · 1987
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The recent excitement about neural networks
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The neurobiological significance of the new learning models
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Can supervised learning be achieved without explicit error back-propagation?
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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The ups and downs of hebb synapses
G. Hinton · 2003
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Homeostatic plasticity in the developing nervous system
G. G. Turrigiano and S. B. Nelson · 2004
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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How auto-encoders could provide credit assignment in deep networks via target propagation
Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2014
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How important is weight symmetry in backpropagation?
Q. Liao, J. Z. Leibo, and T. A. Poggio · 2015
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Learning in the machine: Random backpropagation and the learning channel
P. Baldi, P. J. Sadowski, and Z. Lu · 2016
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Towards deep learning with segregated dendrites
J. Guergiuev, T. P. Lillicrap, and B. A. Richards · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
T. P. Lillicrap, D. Cownden, D. B. Tweed, and C. J. Akerman · 2016
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Direct feedback alignment provides learning in deep neural networks
A. Nø kland · 2016
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Difference target propagation
D.-H. Lee, S. Zhang, A. Fischer, and Y. Bengio · 2015
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L. Xiao, Y. Bahri, J. Sohl-Dickstein, S. S. Schoenholz, and J. Pennington
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Biologically-plausible learning algorithms can scale to large datasets
W. Xiao, H. Chen, Q. Liao, and T. Poggio
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Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
S. Bartunov, A. Santoro, B. A. Richards, G. E. Hinton, and T. Lillicrap · 2018
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Dendritic error backpropagation in deep cortical microcircuits
J. Sacramento, R. Ponte Costa, Y. Bengio, and W. Senn · 2018
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