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Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates.
Scaling up spike-and-slab models for unsupervised feature learning
Goodfellow, I. J., Courville, A. C., and Bengio, Y · 1914
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Hebb, D. O., et al · 1949
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Hippocampus of the brain: Recurrent inhibition in the hippocampus with identification of the inhibitory cell and its synapses
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Single units and sensation: a neuron doctrine for perceptual psychology?
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The existence of persistent states in the brain
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Neural networks and physical systems with emergent collective computational abilities
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A learning algorithm for boltzmann machines
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J · 1985
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Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
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Competitive learning: From interactive activation to adaptive resonance
Grossberg, S · 1987
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Learning representations by recirculation
Hinton, G. E., and McClelland, J. L · 1988
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The recent excitement about neural networks
Crick, F · 1989
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Contrastive hebbian learning in the continuous hopfield model
Movellan, J. R · 1991
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Recurrent excitation in neocortical circuits
Douglas, R. J., Koch, C., Mahowald, M., Martin, K., and Suarez, H. H · 1995
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., and Field, D. J · 1996
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Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
O’Reilly, R. C · 1996
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A synaptically controlled, associative signal for hebbian plasticity in hippocampal neurons
Magee, J. C., and Johnston, D · 1997
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rao, R. P., and Ballard, D. H · 1999
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A universal scaling law between gray matter and white matter of cerebral cortex
Zhang, K., and Sejnowski, T. J · 2000
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Synaptic modification by correlated activity: Hebb’s postulate revisited
Bi, G.-q., and Poo, M.-m · 2001
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Dendritic inhibition enhances neural coding properties
Spratling, M., and Johnson, M · 2001
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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The bayesian brain: the role of uncertainty in neural coding and computation
Knill, D. C., and Pouget, A · 2004
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Bayesian inference in spiking neurons
Deneve, S · 2005
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What kind of graphical model is the brain?
Hinton, G. E., et al · 2005
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A free energy principle for the brain
Friston, K., Kilner, J., and Harrison, L · 2006
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To recognize shapes, first learn to generate images
Hinton, G. E · 2007
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Hierarchical models in the brain
Friston, K · 2008
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The free-energy principle: a rough guide to the brain?
Friston, K · 2009
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Lateral competition for cortical space by layer-specific horizontal circuits
Adesnik, H., and Scanziani, M · 2010
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Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex
London, M., Roth, A., Beeren, L., Häusser, M., and Latham, P. E · 2010
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Action understanding and active inference
Friston, K., Mattout, J., and Kilner, J · 2011
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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Evidence for a hierarchy of predictions and prediction errors in human cortex
Wacongne, C., Labyt, E., van Wassenhove, V., Bekinschtein, T., Naccache, L., and Dehaene, S · 2011
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The predictive processing paradigm has roots in kant
Swanson, L. R · 2016
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A tutorial on the free-energy framework for modelling perception and learning
Bogacz, R · 2017
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The htm spatial pooler—a neocortical algorithm for online sparse distributed coding
Cui, Y., Ahmad, S., and Hawkins, J · 2017
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Towards deep learning with segregated dendrites
Guerguiev, J., Lillicrap, T. P., and Richards, B. A · 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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Interactions between feedback and lateral connections in the primary visual cortex
Liang, H., Gong, X., Chen, M., Yan, Y., Li, W., and Gilbert, C. D · 2017
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Canonical microcircuits for predictive coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., and Friston, K. J · 2012
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A neuronal model of predictive coding accounting for the mismatch negativity
Wacongne, C., Changeux, J.-P., and Dehaene, S · 2012
Cited alongside, same era.
Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Alain, G., and Vincent, P · 2013
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On the challenges of physical implementations of rbms
Dumoulin, V., Goodfellow, I. J., Courville, A., and Bengio, Y · 2013
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Auto-encoding variational bayes
Kingma, D. P., and Welling, M · 2013
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Free energy, precision and learning: the role of cholinergic neuromodulation
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., and Friston, K. J · 2013
Cited alongside, same era.
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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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An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
Whittington, J. C., and Bogacz, R · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Bartunov, S., Santoro, A., Richards, B., Marris, L., Hinton, G. E., and Lillicrap, T · 2018
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Deep learning for classical japanese literature, 2018
Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., and Ha, D · 2018
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Locally coordinated synaptic plasticity of visual cortex neurons in vivo
El-Boustani, S., Ip, J. P., Breton-Provencher, V., Knott, G. W., Okuno, H., Bito, H., and Sur, M · 2018
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Error-gated hebbian rule: A local learning rule for principal and independent component analysis
Isomura, T., and Toyoizumi, T · 2018
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Deep supervised learning using local errors
Mostafa, H., Ramesh, V., and Cauwenberghs, G · 2018
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Deep credit assignment by aligning local representations
Ororbia, A. G., Mali, A., Kifer, D., and Giles, C. L · 2018
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The anatomy of inference: generative models and brain structure
Parr, T., and Friston, K. J · 2018
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Richardson, E., and Weiss, Y · 2018
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Dendritic cortical microcircuits approximate the backpropagation algorithm
Sacramento, J., Ponte Costa, R., Bengio, Y., and Senn, W · 2018
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A classification-based study of covariate shift in gan distributions
Santurkar, S., Schmidt, L., and Madry, A · 2018
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Generalization of equilibrium propagation to vector field dynamics
Scellier, B., Goyal, A., Binas, J., Mesnard, T., and Bengio, Y · 2018
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Local learning in rram neural networks with sparse direct feedback alignment
Crafton, B., West, M., Basnet, P., Vogel, E., and Raychowdhury, A · 2019
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Frenkel, C., Lefebvre, M., and Bol, D · 2019
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Spiking neural predictive coding for continual learning from data streams
Ororbia, A · 2019
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Biologically motivated algorithms for propagating local target representations
Ororbia, A. G., and Mali, A · 2019
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A critique of pure learning and what artificial neural networks can learn from animal brains
Zador, A. M · 2019
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From variational to deterministic autoencoders
Ghosh, P., Sajjadi, M. S., Vergari, A., Black, M., and Schölkopf, B · 2020
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Continual learning of recurrent neural networks by locally aligning distributed representations
Ororbia, A., Mali, A., Giles, C. L., and Kifer, D · 2020
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