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Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagated from the output layer to the input layer.
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The recent excitement about neural networks
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A more biologically plausible learning rule for neural networks
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Backpropagation: The Basic Theory , pp. 1–34
Rumelhart, D. E., Durbin, R., Golden, R., and Chauvin, Y · 1995
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Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network
Xie, X. and Seung, H. S · 2003
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Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S., and Chklovskii, D. B · 2005
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Experience-induced neural circuits that achieve high capacity
Feldman, V. and Valiant, L. G · 2009
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Depression-biased reverse plasticity rule is required for stable learning at top-down connections
Burbank, K. and Kreiman, G · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2012
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The influence of synaptic weight distribution on neuronal population dynamics
Iyer, R., Menon, V., Buice, M., Koch, C., and Mihalas, S · 2013
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The log-dynamic brain: How skewed distributions affect network operations
Buzsáki, G. and Mizuseki, K · 2014
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Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
Gerstner, W., Kistler, W. M., Naud, R., and Paninski, L · 2014
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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How important is weight symmetry in backpropagation?, 2016
Liao, Q., Leibo, J. Z., and Poggio, T · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J · 2016
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Direct feedback alignment provides learning in deep neural networks
Nokland, A · 2016
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Training neural networks with local error signals
Nøkland, A. and Eidnes, L. H · 2019
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Neuroscience-inspired online unsupervised learning algorithms: Artificial neural networks
Pehlevan, C. and Chklovskii, D. B · 2019
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Theories of error back-propagation in the brain
Whittington, J. and Bogacz, R · 2019
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Somatodendritic consistency check for temporal feature segmentation
Asabuki, T. and Fukai, T · 2020
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Decoupled greedy learning of cnns, 2020
Belilovsky, E., Eickenberg, M., and Oyallon, E · 2020
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Preprocessing for deep learning
Jean, H · 2020
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Cholinergic and serotonergic modulation of visual information processing in monkey v1
Shimegi, S., Kimura, A., Sato, A. Y., Aoyama, C., Mizuyama, R., Tsunoda, K., Ueda, F., Araki, S., Goya, R., and Sato, H · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T. P., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D · 2016
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Understanding synthetic gradients and decoupled neural interfaces
Czarnecki, W. M., Swirszcz, G., Jaderberg, M., Osindero, S., Vinyals, O., and Kavukcuoglu, K · 2017
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Decoupled neural interfaces using synthetic gradients
Jaderberg, M., Czarnecki, W. M., Osindero, S., Vinyals, O., Graves, A., Silver, D., and Kavukcuoglu, K · 2017
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Scellier, B. and Bengio, Y · 2017
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Deep supervised learning using local errors
Mostafa, H., Ramesh, V., and Cauwenberghs, G · 2018
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Backpropagation and the brain
Lillicrap, T., Santoro, A., Marris, L., Akerman, C., and Hinton, G · 2020
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Credit assignment through broadcasting a global error vector, 2021
Clark, D. G., Abbott, L. F., and Chung, S · 2021
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Biological and Computer Vision
Kreiman, G · 2021
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Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
Laborieux, A., Ernoult, M., Scellier, B., Bengio, Y., Grollier, J., and Querlioz, D · 2021
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Credit assignment in neural networks through deep feedback control
Meulemans, A., Tristany Farinha, M., Garcia Ordonez, J., Vilimelis Aceituno, P., Sacramento, J. a., and Grewe, B. F · 2021
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Align, then memorise: the dynamics of learning with feedback alignment
Refinetti, M., D’Ascoli, S., Ohana, R., and Goldt, S · 2021
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Mesoscopic physiological interactions in the human brain reveal small world properties
Wang, J., Tao, A., Anderson, W., Madsen, J., and Kreiman, G · 2021
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Introducing principles of synaptic integration in the optimization of deep neural networks
Dellaferrera, G., Woźniak, S., Indiveri, G., Pantazi, A., and Eleftheriou, E · 2022
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