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Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century.
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Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type
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Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects
Rao, RPN and Ballard, DH · 1999
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The role of Occam’s razor in knowledge discovery
Domingos, P · 1999
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An optimal estimation approach to visual perception and learning
Rao, RPN · 1999
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A model of predictive coding based on spike timing
Ballard, D, Rao, R, and Zhang, Z · 1999
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Bayesian model averaging: A tutorial (with comments by M. Clyde, David Draper and EI George, and a rejoinder by the authors
Hoeting, JA, Madigan, D, Raftery, AE, and Volinsky, CT · 1999
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A single-spike model of predictive coding
Ballard, DH, Rao, RPN, and Zhang, Z · 2000
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Bayesian model selection and model averaging
Wasserman, L · 2000
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Modified Kalman filter based method for training state-recurrent multilayer perceptrons
Erdogmus, D, Sanchez, JC, and Principe, JC · 2002
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Probabilistic models of the brain: Perception and neural function
Rao, RPN, Olshausen, BA, and Lewicki, MS · 2002
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A perspective view and survey of meta-learning
Vilalta, R and Drissi, Y · 2002
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Learning and inference in the brain
Friston, K · 2003
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Hierarchical Bayesian inference in the visual cortex
Lee, TS and Mumford, D · 2003
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Variational message passing
Winn, J, Bishop, CM, and Jaakkola, T · 2005
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A theory of cortical responses
Friston, K · 2005
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Hierarchical Dirichlet processes
Teh, YW, Jordan, MI, Beal, MJ, and Blei, DM · 2006
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Bayesian brain: Probabilistic approaches to neural coding
Doya, K · 2007
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Correlation between neural spike trains increases with firing rate
De La Rocha, J, Doiron, B, Shea-Brown, E, Josić, K, and Reyes, A · 2007
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On variational message passing on factor graphs
Dauwels, J · 2007
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Nonparametric Bayesian Models of Lexican Acquisition
Goldwater, SJ · 2007
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Hierarchical models in the brain
Friston, K · 2008
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DEM: A variational treatment of dynamic systems
Friston, KJ, Trujillo-Barreto, N, and Daunizeau, J · 2008
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Predictive coding explains binocular rivalry: An epistemological review
Hohwy, J, Roepstorff, A, and Friston, K · 2008
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The missing memristor found
Strukov, DB, Snider, GS, Stewart, DR, and Williams, RS · 2008
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Vector symbolic architectures: A new building material for artificial general intelligence
Levy, SD and Gayler, R · 2008
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Predictive coding under the free-energy principle
Friston, K and Kiebel, S · 2009
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Perception and hierarchical dynamics
Kiebel, SJ, Daunizeau, J, and Friston, KJ · 2009
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The mismatch negativity: A review of underlying mechanisms
Garrido, MI, Kilner, JM, Stephan, KE, and Friston, KJ · 2009
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Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors
Kanerva, P · 2009
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The free-energy principle: A unified brain theory?
Friston, K · 2010
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Mathematical Foundations of Neuroscience
Ermentrout, B and Terman, DH · 2010
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Predictive coding as a model of response properties in cortical area V1
Spratling, MW · 2010
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Attention, uncertainty, and free-energy
Feldman, H and Friston, KJ · 2010
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Fast inference in sparse coding algorithms with applications to object recognition
Kavukcuoglu, K, Ranzato, M, and LeCun, Y · 2010
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Generalised filtering
Friston, K, Stephan, K, Li, B, Daunizeau, J, et al · 2010
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Ten simple rules for dynamic causal modeling
Stephan, KE, Penny, WD, Moran, RJ, den Ouden, HE, Daunizeau, J, and Friston, KJ · 2010
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Action understanding and active inference
Friston, K, Mattout, J, and Kilner, J · 2011
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How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, JB, Kemp, C, Griffiths, TL, and Goodman, ND · 2011
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Free energy and dendritic self-organization
Kiebel, SJ and Friston, KJ · 2011
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Post hoc Bayesian model selection
Friston, K and Penny, W · 2011
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Multi-column deep neural networks for image classification
Ciregan, D, Meier, U, and Schmidhuber, J · 2012
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A, Sutskever, I, and Hinton, GE · 2012
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Temporal context in object recognition
Chalasani, R and Principe, JC · 2012
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Perceptions as hypotheses: Saccades as experiments
Friston, K, Adams, RA, Perrinet, L, and Breakspear, M · 2012
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The history of the future of the Bayesian brain
Friston, K · 2012
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A neuronal model of predictive coding accounting for the mismatch negativity
Wacongne, C, Changeux, JP, and Dehaene, S · 2012
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Canonical microcircuits for predictive coding
Bastos, AM, Usrey, WM, Adams, RA, Mangun, GR, Fries, P, and Friston, KJ · 2012
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Predictive coding, precision and synchrony
Friston, K · 2012
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Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T, Hinton, G, et al · 2012
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A tutorial on Bayesian nonparametric models
Gershman, SJ and Blei, DM · 2012
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Comparing dynamic causal models using AIC, BIC and free energy
Penny, WD · 2012
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Auto-encoding variational Bayes
Kingma, DP and Welling, M · 2013
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Deep predictive coding networks
Chalasani, R and Principe, JC · 2013
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Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A · 2013
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Active inference, sensory attenuation and illusions
Brown, H, Adams, RA, Parees, I, Edwards, M, and Friston, K · 2013
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Free energy, precision and learning: the role of cholinergic neuromodulation
Moran, RJ, Campo, P, Symmonds, M, Stephan, KE, Dolan, RJ, and Friston, KJ · 2013
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Reflections on agranular architecture: Predictive coding in the motor cortex
Shipp, S, Adams, RA, and Friston, KJ · 2013
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On the difficulty of training recurrent neural networks
Pascanu, R, Mikolov, T, and Bengio, Y · 2013
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Statistical analysis of stochastic gradient methods for generalized linear models
Toulis, P, Airoldi, E, and Rennie, J · 2014
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Cognitive architectures for sensory processing
Principe, JC and Chalasani, R · 2014
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Context dependent encoding using convolutional dynamic networks
Chalasani, R and Principe, JC · 2014
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Active inference, eye movements and oculomotor delays
Perrinet, LU, Adams, RA, and Friston, KJ · 2014
Cited alongside, same era.
The cybernetic bayesian brain
Seth, AK · 2014
Cited alongside, same era.
The SpiNNaker project
Furber, SB, Galluppi, F, Temple, S, and Plana, LA · 2014
Cited alongside, same era.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, PA, Arthur, JV, Alvarez-Icaza, R, Cassidy, AS, Sawada, J, Akopyan, F, Jackson, BL, Imam, N, Guo, C, Nakamura, Y, et al · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N, Hinton, G, Krizhevsky, A, Sutskever, I, and Salakhutdinov, R · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, DP and Ba, J · 2014
Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network
Ficco, L, Mancuso, L, Manuello, J, Teneggi, A, Liloia, D, Duca, S, Costa, T, Kovacs, GZ, and Cauda, F · 2021
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Tightening the biological constraints on gradient-based predictive coding
Alonso, N and Neftci, E · 2021
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Meta-learning in neural networks: A survey
Hospedales, T, Antoniou, A, Micaelli, P, and Storkey, A · 2021
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Dynamic expectation maximization algorithm for estimation of linear systems with colored noise
Anil Meera, A and Wisse, M · 2021
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Predprop: Bidirectional stochastic optimization with precision weighted predictive coding
Ofner, A and Stober, S · 2021
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Cited alongside, same era.
Towards biologically plausible deep learning
Bengio, Y, Lee, DH, Bornschein, J, Mesnard, T, and Lin, Z · 2015
Cited alongside, same era.
Parallel flow in deep predictive coding networks
Santana, E, Cinar, GT, and Principe, JC · 2015
Cited alongside, same era.
A neural model of binocular saccade planning and vergence control
Muhammad, W and Spratling, MW · 2015
Cited alongside, same era.
Is predictive coding theory articulated enough to be testable?, 2015
Kogo, N and Trengove, C · 2015
Cited alongside, same era.
Cerebral hierarchies: Predictive processing, precision and the pulvinar
Kanai, R, Komura, Y, Shipp, S, and Friston, K · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S and Szegedy, C · 2015
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R, Blattmann, A, Lorenz, D, Esser, P, and Ommer, B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C, Chan, W, Saxena, S, Li, L, Whang, J, Denton, EL, Ghasemipour, K, Gontijo Lopes, R, Karagol Ayan, B, Salimans, T, et al · 2022
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Imagen video: High definition video generation with diffusion models
Ho, J, Chan, W, Saharia, C, Whang, J, Gao, R, Gritsenko, A, Kingma, DP, Poole, B, Norouzi, M, Fleet, DJ, et al · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A, Dhariwal, P, Nichol, A, Chu, C, and Chen, M · 2022
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Human-level play in the game of diplomacy by combining language models with strategic reasoning
(FAIR)†, MFARDT, Bakhtin, A, Brown, N, Dinan, E, Farina, G, Flaherty, C, Fried, D, Goff, A, Gray, J, Hu, H, et al · 2022
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Mastering the game of stratego with model-free multiagent reinforcement learning
Perolat, J, De Vylder, B, Hennes, D, Tarassov, E, Strub, F, de Boer, V, Muller, P, Connor, JT, Burch, N, Anthony, T, et al · 2022
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The forward-forward algorithm: Some preliminary investigations
Hinton, G · 2022
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Toward next-generation artificial intelligence: Catalyzing the NeuroAI revolution
Zador, A, Richards, B, Ölveczky, B, Escola, S, Bengio, Y, Boahen, K, Botvinick, M, Chklovskii, D, Churchland, A, Clopath, C, et al · 2022
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Designing ecosystems of intelligence from first principles
Friston, KJ, Ramstead, MJD, Kiefer, AB, Tschantz, A, Buckley, CL, Albarracin, M, Pitliya, RJ, Heins, C, Klein, B, Millidge, B, et al · 2022
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How active inference could help revolutionise robotics
Da Costa, L, Lanillos, P, Sajid, N, Friston, K, and Khan, S · 2022
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Predictive coding: Towards a future of deep learning beyond backpropagation?
Millidge, B, Salvatori, T, Song, Y, Bogacz, R, and Lukasiewicz, T · 2022
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The neural coding framework for learning generative models
Ororbia, A and Kifer, D · 2022
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Learning on arbitrary graph topologies via predictive coding
Salvatori, T, Pinchetti, L, Millidge, B, Song, Y, Bao, T, Bogacz, R, and Lukasiewicz, T · 2022
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A theoretical framework for inference learning
Alonso, N, Millidge, B, Krichmar, J, and Neftci, EO · 2022
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Predictive coding beyond Gaussian assumptions
Pinchetti, L, Salvatori, T, Millidge, B, Song, Y, Yordanov, Y, and Lukasiewicz, T · 2022
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Incremental predictive coding: A parallel and fully automatic learning algorithm
Salvatori, T, Song, Y, Millidge, B, Xu, Z, Sha, L, Emde, C, Bogacz, R, and Lukasiewicz, T · 2022
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Robust graph representation learning via predictive coding
Byiringiro, B, Salvatori, T, and Lukasiewicz, T · 2022
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Universal Hopfield networks: A general framework for single-shot associative memory models
Millidge, B, Salvatori, T, Song, Y, Lukasiewicz, T, and Bogacz, R · 2022
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BayesPCN: A continually learnable predictive coding associative memory
Yoo, J and Wood, F · 2022
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On the relationship between variational inference and auto-associative memory
Annabi, L, Pitti, A, and Quoy, M · 2022
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On the relationship between predictive coding and backpropagation
Rosenbaum, R · 2022
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Reverse differentiation via predictive coding
Salvatori, T, Song, Y, Xu, Z, Lukasiewicz, T, and Bogacz, R · 2022
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Lifelong neural predictive coding: Learning cumulatively online without forgetting
Ororbia, A, Mali, A, Giles, CL, and Kifer, D · 2022
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Cogngen: Building the kernel for a hyperdimensional predictive processing cognitive architecture
Ororbia, A and Kelly, MA · 2022
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Dynamic predictive coding: A new model of hierarchical sequence learning and prediction in the cortex
Jiang, LP and Rao, RPN · 2022
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Backprop-free reinforcement learning with active neural generative coding
Ororbia, AG and Mali, A · 2022
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Rao, RPN, Gklezakos, DC, and Sathish, V · 2022
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Gklezakos, DC and Rao, RPN · 2022
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Beyond backpropagation: Bilevel optimization through implicit differentiation and equilibrium propagation
Zucchet, N and Sacramento, J · 2022
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Dynamical memristors for higher-complexity neuromorphic computing
Kumar, S, Wang, X, Strachan, JP, Yang, Y, and Lu, WD · 2022
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Neuronal cultures playing pong: First steps toward advanced screening and biological computing
Smirnova, L and Hartung, T · 2022
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Lan, M, Xiong, X, Jiang, Z, and Lou, Y · 2022
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Dendritic predictive coding: A theory of cortical computation with spiking neurons
Mikulasch, FA, Rudelt, L, Wibral, M, and Priesemann, V · 2022
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(Non-)Convergence results for predictive coding networks
Frieder, S and Lukasiewicz, T · 2022
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Structure learning in predictive processing needs revision
Rutar, D, de Wolff, E, van Rooij, I, and Kwisthout, J · 2022
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H, Martin, L, Stone, K, Albert, P, Almahairi, A, Babaei, Y, Bashlykov, N, Batra, S, Bhargava, P, Bhosale, S, et al · 2023
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Inferring neural activity before plasticity: A foundation for learning beyond backpropagation
Song, Y, Millidge, BG, Salvatori, T, Lukasiewicz, T, Xu, Z, and Bogacz, R · 2023
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Understanding predictive coding as an adaptive trust-region method
Innocenti, F, Singh, R, and Buckley, CL · 2023
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Sample as you infer: Predictive coding with Langevin dynamics
Zahid, U, Guo, Q, and Fountas, Z · 2023
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Hybrid predictive coding: Inferring, fast and slow
Tscshantz, A, Millidge, B, Seth, AK, and Buckley, CL · 2023
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Predictive coding beyond correlations
Salvatori, T, Pinchetti, L, M’Charrak, A, Millidge, B, and Lukasiewicz, T · 2023
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A theoretical framework for inference and learning in predictive coding networks
Millidge, B, Song, Y, Salvatori, T, Lukasiewicz, T, and Bogacz, R · 2023
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Recurrent predictive coding models for associative memory employing covariance learning
Tang, M, Salvatori, T, Millidge, B, Song, Y, Lukasiewicz, T, and Bogacz, R · 2023
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Backpropagation at the infinitesimal inference limit of energy-based models: Unifying predictive coding, equilibrium propagation, and contrastive Hebbian learning
Millidge, B, Song, Y, Salvatori, T, Lukasiewicz, T, and Bogacz, R · 2023
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Predictive coding as a neuromorphic alternative to backpropagation: A critical evaluation
Zahid, U, Guo, Q, and Fountas, Z · 2023
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Convolutional neural generative coding: Scaling predictive coding to natural images
Ororbia, A and Mali, A · 2023
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Deep kinematic inference affords efficient and scalable control of bodily movements
Priorelli, M, Pezzulo, G, and Stoianov, IP · 2023
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Efficient motor learning through action-perception cycles in deep kinematic inference
Priorelli, M and Stoianov, IP · 2023
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Active predictive coding: Brain-inspired reinforcement learning for sparse reward robotic control problems
Ororbia, A and Mali, A · 2023
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Maze learning using a hyperdimensional predictive processing cognitive architecture
Ororbia, AG and Kelly, MA · 2023
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Evidence of a predictive coding hierarchy in the human brain listening to speech
Caucheteux, C, Gramfort, A, and King, JR · 2023
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Where is the error? Hierarchical predictive coding through dendritic error computation
Mikulasch, FA, Rudelt, L, Wibral, M, and Priesemann, V · 2023
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Preventing deterioration of classification accuracy in predictive coding networks
Kinghorn, PF, Millidge, B, and Buckley, CL · 2023
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Spiking neural predictive coding for continually learning from data streams
Ororbia, A · 2023
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Contrastive-signal-dependent plasticity: Forward-forward learning of spiking neural systems
Ororbia, A · 2023
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Organoid intelligence (OI): The new frontier in biocomputing and intelligence-in-a-dish
Smirnova, L, Caffo, BS, Gracias, DH, Huang, Q, Morales Pantoja, IE, Tang, B, Zack, DJ, Berlinicke, CA, Boyd, JL, Harris, TD, et al · 2023
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The sentient organoid?
Friston, K · 2023
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Understanding and improving optimization in predictive coding networks
Alonso, N, Krichmar, J, and Neftci, E · 2023
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Curvature-sensitive predictive coding with approximate Laplace Monte Carlo
Zahid, U, Guo, Q, Friston, K, and Fountas, Z · 2023
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Poisson variational autoencoder
Vafaii, H, Galor, D, and Yates, J · 2024
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Divide-and-conquer predictive coding: A structured Bayesian inference algorithm
Sennesh, E, Wu, H, and Salvatori, T · 2024
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Learning probability distributions of sensory inputs with Monte Carlo predictive coding
Oliviers, G, Bogacz, R, and Meulemans, A · 2024
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Tight stability, convergence, and robustness bounds for predictive coding networks
Mali, A, Salvatori, T, and Ororbia, A · 2024
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Benchmarking predictive coding networks–made simple
Pinchetti, L, Qi, C, Lokshyn, O, Olivers, G, Emde, C, Tang, M, M’Charrak, A, Frieder, S, Menzat, B, Bogacz, R, et al · 2024
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JPC: Flexible inference for predictive coding networks in JAX
Innocenti, F, Kinghorn, P, Yun-Farmbrough, W, Varona, MDL, Singh, R, and Buckley, CL · 2024
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Brain-like learning with exponentiated gradients
Cornford, J, Pogodin, R, Ghosh, A, Sheng, K, Bicknell, BA, Codol, O, Clark, BA, Lajoie, G, and Richards, BA · 2024
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pyhgf: A neural network library for predictive coding
Legrand, N, Weber, L, Waade, PT, Daugaard, AHM, Khodadadi, M, Mikuš, N, and Mathys, C · 2024
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Predictive coding with spiking neural networks: A survey
N’dri, AW, Gebhardt, W, Teulière, C, Zeldenrust, F, Rao, RP, Triesch, J, and Ororbia, A · 2024
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Brain-like variational inference
Vafaii, H, Galor, D, and Yates, JL · 2025
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Bethe predictive coding
Koudahl, M, Salvatori, T, Da Costa, L, Beck, J, Tschantz, A, Buckley, CL, and Linander, H · 2025
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μ \mu pc: Scaling predictive coding to 100+ layer networks
Innocenti, F, Achour, EM, and Buckley, CL · 2025
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Training deep predictive coding networks
Qi, C, Lukasiewicz, T, and Salvatori, T · 2025
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Error optimization: Overcoming exponential signal decay in deep predictive coding networks
Goemaere, C, Oliviers, G, Bogacz, R, and Demeester, T · 2025
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Predictive coding: A more cognitive process than we thought?
Gabhart, KM, Xiong, YS, and Bastos, AM · 2025
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Tschantz, A, Koudahl, M, Linander, H, Da Costa, L, Heins, C, Beck, J, and Buckley, C · 2025
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