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We propose a novel biologically-plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks.
Training neural networks with local error signals
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Learning representations by back-propagating errors
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A mean field theory learning algorithm for neural networks
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Learning representations by recirculation
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The recent excitement about neural networks
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Contrastive learning and neural oscillations
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Contrastive hebbian learning in the continuous hopfield model
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Principles of neural science
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Spike-based learning rules and stabilization of persistent neural activity
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Equivalence of backpropagation and contrastive hebbian learning in a layered network
Xie, X. and Seung, H. S. (2003) · 2003
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Sparse coding of sensory inputs
Olshausen, B. A. and Field, D. J. (2004) · 2004
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Matching categorical object representations in inferior temporal cortex of man and monkey
Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., and Bandettini, P. A. (2008) · 2008
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Learning multiple layers of features from tiny images
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Mnist handwritten digit database
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The functional architecture of the ventral temporal cortex and its role in categorization
Grill-Spector, K. and Weiner, K. S. (2014) · 2014
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Stdp as presynaptic activity times rate of change of postsynaptic activity
Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., and Wu, Y. (2015) · 2015
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Difference target propagation
Lee, D.-H., Zhang, S., Fischer, A., and Bengio, Y. (2015) · 2015
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A normative theory of adaptive dimensionality reduction in neural networks
Pehlevan, C. and Chklovskii, D. (2015) · 2015
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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) · 2016
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Dendritic cortical microcircuits approximate the backpropagation algorithm
Sacramento, J., Costa, R. P., Bengio, Y., and Senn, W. (2018) · 2018
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Manifold-tiling localized receptive fields are optimal in similarity-preserving neural networks
Sengupta, A., Pehlevan, C., Tepper, M., Genkin, A., and Chklovskii, D. (2018) · 2018
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Neuromodulators and long-term synaptic plasticity in learning and memory: A steered-glutamatergic perspective
Bazzari, A. H. and Parri, H. R. (2019) · 2019
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Neuromodulation of spike-timing-dependent plasticity: past, present, and future
Brzosko, Z., Mierau, S. B., and Paulsen, O. (2019) · 2019
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Updates of equilibrium prop match gradients of backprop through time in an rnn with static input
Ernoult, M., Grollier, J., Querlioz, D., Bengio, Y., and Scellier, B. (2019) · 2019
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Nøkland, A. (2016) · 2016
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Theano: A python framework for fast computation of mathematical expressions
Team, T. T. D., Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., et al. (2016) · 2016
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Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. and DiCarlo, J. J. (2016) · 2016
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Towards deep learning with segregated dendrites
Guerguiev, J., Lillicrap, T. P., and Richards, B. A. (2017) · 2017
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Blind nonnegative source separation using biological neural networks
Pehlevan, C., Mohan, S., and Chklovskii, D. B. (2017) · 2017
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Scellier, B. and Bengio, Y. (2017) · 2017
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An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
Whittington, J. C. and Bogacz, R. (2017) · 2017
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A neural network for semi-supervised learning on manifolds
Genkin, A., Sengupta, A. M., and Chklovskii, D. (2019) · 2019
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Learning from brains how to regularize machines
Li, Z., Brendel, W., Walker, E., Cobos, E., Muhammad, T., Reimer, J., Bethge, M., Sinz, F., Pitkow, Z., and Tolias, A. (2019) · 2019
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Structured and deep similarity matching via structured and deep hebbian networks
Obeid, D., Ramambason, H., and Pehlevan, C. (2019) · 2019
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Biologically motivated algorithms for propagating local target representations
Ororbia, A. G. and Mali, A. (2019) · 2019
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Neuroscience-inspired online unsupervised learning algorithms: Artificial neural networks
Pehlevan, C. and Chklovskii, D. B. (2019) · 2019
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Dendritic solutions to the credit assignment problem
Richards, B. A. and Lillicrap, T. P. (2019) · 2019
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Theories of error back-propagation in the brain
Whittington, J. C. and Bogacz, R. (2019) · 2019
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Backpropagation and the brain
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., and Hinton, G. (2020) · 2020
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