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Unexpected stimuli induce "error" or "surprise" signals in the brain.
Predictive coding: A fresh view of inhibition in the retina
M. V. Srinivasan, S. B. Laughlin, and A. Dubs · 1982
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Reliability of spike timing in neocortical neurons
Zachary F Mainen and Terrence J Sejnowski · 1995
Earlier work this paper cites.
A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants , page 355–368
Radford M. Neal and Geoffrey E. Hinton · 1998
Earlier work this paper cites.
A winner-take-all circuit with controllable soft max property
Shih-Chii Liu · 1999
Earlier work this paper cites.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh P N Rao and Dana H Ballard · 1999
Earlier work this paper cites.
Neuronal oscillations in cortical networks
György Buzsáki and Andreas Draguhn · 2004
Earlier work this paper cites.
Neuronal circuits of the neocortex
Rodney J Douglas and Kevan AC Martin · 2004
Earlier work this paper cites.
A theory of cortical responses
Karl Friston · 2005
Earlier work this paper cites.
Probabilistic models of cognition: Conceptual foundations
Nick Chater, Joshua B Tenenbaum, and Alan Yuille · 2006
Earlier work this paper cites.
Existence and construction of dynamical potential in nonequilibrium processes without detailed balance
L. Yin and P. Ao · 2006
Earlier work this paper cites.
Fractional differentiation by neocortical pyramidal neurons
Brian N. Lundstrom, Matthew H. Higgs, William J. Spain, and Adrienne L. Fairhall · 2008
Earlier work this paper cites.
Neural implementation of hierarchical bayesian inference by importance sampling
Lei Shi and Thomas L. Griffiths · 2009
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Parallel gibbs sampling: From colored fields to thin junction trees
Joseph Gonzalez, Yucheng Low, Arthur Gretton, and Carlos Guestrin · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh · 2011
Earlier work this paper cites.
Canonical microcircuits for predictive coding
André M. Bastos, W Martin Usrey, Rick A Adams, George R Mangun, Pascal Fries, and Karl J Friston · 2012
Earlier work this paper cites.
A family of mcmc methods on implicitly defined manifolds
Marcus Brubaker, Mathieu Salzmann, and Raquel Urtasun · 2012
Earlier work this paper cites.
Normalization as a canonical neural computation
Matteo Carandini and David J Heeger · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Probabilistic brains: knowns and unknowns
Alexandre Pouget, Jeffrey M Beck, Wei Ji Ma, and Peter E Latham · 2013
Earlier work this paper cites.
Communication through coherence with inter-areal delays
Andre M Bastos, Julien Vezoli, and Pascal Fries · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2014
Earlier work this paper cites.
Rhythms for cognition: Communication through coherence
Pascal Fries · 2015
Earlier work this paper cites.
Deep learning
Yann Lecun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Nested sequential monte carlo methods
Christian Naesseth, Fredrik Lindsten, and Thomas Schon · 2015
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A review of predictive coding algorithms
M. W. Spratling · 2015
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Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
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Divide-and-conquer with sequential monte carlo
F. Lindsten, A. M. Johansen, C. A. Naesseth, B. Kirkpatrick, T. B. Schön, J. A.D. Aston, and A. Bouchard-Côté · 2016
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Fold-change detection in biological systems
Miri Adler and Uri Alon · 2017
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Simple and effective vae training with calibrated decoders
Oleh Rybkin, Kostas Daniilidis, and Sergey Levine · 2021
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Learning proposals for probabilistic programs with inference combinators
Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, and Jan-Willem Van de Meent · 2021
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Oops I took a gradient: Scalable sampling for discrete distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris Maddison · 2021
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Nested variational inference
Heiko Zimmermann, Hao Wu, Babak Esmaeili, and Jan-Willem van de Meent · 2021
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Local connectivity and synaptic dynamics in mouse and human neocortex
Luke Campagnola, Stephanie C. Seeman, Thomas Chartrand, Lisa Kim, Alex Hoggarth, Clare Gamlin, Shinya Ito, Jessica Trinh, Pasha Davoudian, Cristina Radaelli, Mean-Hwan Kim, Travis Hage, Thomas Braun, Lauren Alfiler, Julia Andrade, Phillip Bohn, Rachel Dalley, Alex Henry, Sara Kebede, Alice Mukora, David Sandman, Grace Williams, Rachael Larsen, Corinne Teeter, Tanya L. Daigle, Kyla Berry, Nadia Dotson, Rachel Enstrom, Melissa Gorham, Madie Hupp, Samuel Dingman Lee, Kiet Ngo, Philip R. Nicovich, Lydia Potekhina, Shea Ransford, Amanda Gary, Jeff Goldy, Delissa McMillen, Trangthanh Pham, Michael Tieu, La’Akea Siverts, Miranda Walker, Colin Farrell, Martin Schroedter, Cliff Slaughterbeck, Charles Cobb, Richard Ellenbogen, Ryder P. Gwinn, C. Dirk Keene, Andrew L. Ko, Jeffrey G. Ojemann, Daniel L. Silbergeld, Daniel Carey, Tamara Casper, Kirsten Crichton, Michael Clark, Nick Dee, Lauren Ellingwood, Jessica Gloe, Matthew Kroll, Josef Sulc, Herman Tung, Katherine Wadhwani, Krissy Brouner, Tom Egdorf, Michelle Maxwell, Medea McGraw, Christina Alice Pom, Augustin Ruiz, Jasmine Bomben, David Feng, Nika Hejazinia, Shu Shi, Aaron Szafer, Wayne Wakeman, John Phillips, Amy Bernard, Luke Esposito, Florence D. D’Orazi, Susan Sunkin, Kimberly Smith, Bosiljka Tasic, Anton Arkhipov, Staci Sorensen, Ed Lein, Christof Koch, Gabe Murphy, Hongkui Zeng, and Tim Jarsky · 2022
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Cited alongside, same era.
Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
Cited alongside, same era.
An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity
James CR Whittington and Rafal Bogacz · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Iterative amortized inference
Joseph Marino, Yisong Yue, and Stephan Mandt · 2018
Cited alongside, same era.
Variational sequential monte carlo
Christian Naesseth, Scott Linderman, Rajesh Ranganath, and David Blei · 2018
Cited alongside, same era.
Faithful inversion of generative models for effective amortized inference
Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh, and Frank Wood · 2018
Cited alongside, same era.
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Learning and inference in sparse coding models with langevin dynamics
Michael Y-S Fang, Mayur Mudigonda, Ryan Zarcone, Amir Khosrowshahi, and Bruno A Olshausen · 2022
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Memoryless optimality: Neurons do not need adaptation to optimally encode stimuli with arbitrarily complex statistics
Oren Forkosh · 2022
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Constrained predictive coding as a biologically plausible model of the cortical hierarchy
Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan Sengupta, and Dmitri Chklovskii · 2022
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The neural coding framework for learning generative models
Alexander Ororbia and Daniel Kifer · 2022
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Convolutional neural generative coding: Scaling predictive coding to natural images
Alexander Ororbia and Ankur Mali · 2022
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Predictive coding beyond gaussian distributions
Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov, Beren Millidge, Yuhang Song, and Thomas Lukasiewicz · 2022
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Learning on arbitrary graph topologies via predictive coding
Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song, Tianyi Bao, Rafal Bogacz, and Thomas Lukasiewicz · 2022
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Langevin autoencoders for learning deep latent variable models
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, and Yutaka Matsuo · 2022
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Neural Sampling in Hierarchical Exponential-family Energy-based Models
Xingsi Dong and Si Wu · 2023
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Particle algorithms for maximum likelihood training of latent variable models
Juan Kuntz, Jen Ning Lim, and Adam M Johansen · 2023
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A theoretical framework for inference and learning in predictive coding networks
Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, and Rafal Bogacz · 2023
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Brain-inspired computational intelligence via predictive coding
Tommaso Salvatori, Ankur Mali, Christopher L Buckley, Thomas Lukasiewicz, Rajesh PN Rao, Karl Friston, and Alexander Ororbia · 2023
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Discrete Langevin Samplers via Wasserstein Gradient Flow
Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, and Dale Schuurmans · 2023
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Optimal preconditioning and fisher adaptive langevin sampling
Michalis Titsias · 2023
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Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation
Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, and Ruqi Zhang · 2023
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Cortical depth profiles in primary visual cortex for illusory and imaginary experiences
Johanna Bergmann, Lucy S Petro, Clement Abbatecola, Min S Li, A Tyler Morgan, and Lars Muckli · 2024
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The divide-and-conquer sequential Monte Carlo algorithm: Theoretical properties and limit theorems
Juan Kuntz, Francesca R. Crucinio, and Adam M. Johansen · 2024
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Learning probability distributions of sensory inputs with monte carlo predictive coding
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How does the primate brain combine generative and discriminative computations in vision?
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A stable, fast, and fully automatic learning algorithm for predictive coding networks
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Inferring neural activity before plasticity as a foundation for learning beyond backpropagation
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Sample as you infer: Predictive coding with langevin dynamics
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