Fetching the paper…
Reading the bibliography…
Predictive Coding (PC) offers a brain-inspired alternative to backpropagation for neural network training, described as a physical system minimizing its internal energy.
“Predictive coding as a neuromorphic alternative to backpropagation: a critical evaluation”
Umais Zahid, Qinghai Guo and Zafeirios Fountas · 1909
Earlier work this paper cites.
“Predictive coding as a neuromorphic alternative to backpropagation: a critical evaluation”
Umais Zahid, Qinghai Guo and Zafeirios Fountas · 1909
Earlier work this paper cites.
“Predictive coding as a neuromorphic alternative to backpropagation: a critical evaluation”
Umais Zahid, Qinghai Guo and Zafeirios Fountas · 1909
Earlier work this paper cites.
“The MNIST database of handwritten digits”
Yann LeCun · 1998
Earlier work this paper cites.
“The MNIST database of handwritten digits”
Yann LeCun · 1998
Earlier work this paper cites.
“The MNIST database of handwritten digits”
Yann LeCun · 1998
Earlier work this paper cites.
“Predictive coding under the free-energy principle”
Karl Friston and Stefan Kiebel · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images”
Alex Krizhevsky · 2009
Earlier work this paper cites.
“Predictive coding under the free-energy principle”
Karl Friston and Stefan Kiebel · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images”
Alex Krizhevsky · 2009
Earlier work this paper cites.
“Predictive coding under the free-energy principle”
Karl Friston and Stefan Kiebel · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images”
Alex Krizhevsky · 2009
Earlier work this paper cites.
“Auto-encoding variational Bayes”
Diederik. Kingma and Max Welling · 2013
Earlier work this paper cites.
“Auto-encoding variational Bayes”
Diederik. Kingma and Max Welling · 2013
Earlier work this paper cites.
“Auto-encoding variational Bayes”
Diederik. Kingma and Max Welling · 2013
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Gaussian Error Linear Units (GELUs)”
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Gaussian Error Linear Units (GELUs)”
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Gaussian Error Linear Units (GELUs)”
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
“A tutorial on the free-energy framework for modelling perception and learning”
Rafal Bogacz · 2017
Earlier work this paper cites.
“EMNIST: Extending MNIST to handwritten letters”
Gregory Cohen, Saeed Afshar, Jonathan Tapson and Andre Van · 2017
Earlier work this paper cites.
“An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity”
James.. Whittington and Rafal Bogacz · 2017
Earlier work this paper cites.
“Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms”
Han Xiao, Kashif Rasul and Roland Vollgraf · 2017
Earlier work this paper cites.
“A tutorial on the free-energy framework for modelling perception and learning”
Rafal Bogacz · 2017
Earlier work this paper cites.
“EMNIST: Extending MNIST to handwritten letters”
Gregory Cohen, Saeed Afshar, Jonathan Tapson and Andre Van · 2017
Earlier work this paper cites.
“An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity”
James.. Whittington and Rafal Bogacz · 2017
Earlier work this paper cites.
“Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms”
Han Xiao, Kashif Rasul and Roland Vollgraf · 2017
Earlier work this paper cites.
“A tutorial on the free-energy framework for modelling perception and learning”
Rafal Bogacz · 2017
Earlier work this paper cites.
“EMNIST: Extending MNIST to handwritten letters”
Gregory Cohen, Saeed Afshar, Jonathan Tapson and Andre Van · 2017
Earlier work this paper cites.
“An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity”
James.. Whittington and Rafal Bogacz · 2017
Earlier work this paper cites.
“Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms”
Han Xiao, Kashif Rasul and Roland Vollgraf · 2017
Cited alongside, same era.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
“Theories of error back-propagation in the brain”
James.. Whittington and Rafal Bogacz · 2019
Cited alongside, same era.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
“Theories of error back-propagation in the brain”
James.. Whittington and Rafal Bogacz · 2019
Cited alongside, same era.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
“Learning probability distributions of sensory inputs with Monte Carlo Predictive Coding”
Gaspard Oliviers, Rafal Bogacz and Alexander Meulemans · 2024
Later among the works it cites.
“Incremental Predictive Coding: A parallel and fully automatic learning algorithm”
Tommaso Salvatori et al · 2024
Later among the works it cites.
“Inferring neural activity before plasticity as a foundation for learning beyond backpropagation”
Yuhang Song et al · 2024
Later among the works it cites.
“Sample as you Infer: Predictive Coding with Langevin Dynamics”
Umais Zahid, Qinghai Guo and Zafeirios Fountas · 2024
Later among the works it cites.
“Understanding and Improving Optimization in Predictive Coding Networks”
Nicholas Alonso, Jeffrey Krichmar and Emre Neftci · 2024
Later among the works it cites.
“JPC: Flexible Inference for Predictive Coding Networks in JAX”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Theories of error back-propagation in the brain”
James.. Whittington and Rafal Bogacz · 2019
Cited alongside, same era.
“Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks”
Wei Hu, Lechao Xiao and Jeffrey Pennington · 2020
Cited alongside, same era.
“Can the Brain Do Backpropagation? — Exact Implementation of Backpropagation in Predictive Coding Networks”
Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu and Rafal Bogacz · 2020
Cited alongside, same era.
“Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks”
Wei Hu, Lechao Xiao and Jeffrey Pennington · 2020
Cited alongside, same era.
“Can the Brain Do Backpropagation? — Exact Implementation of Backpropagation in Predictive Coding Networks”
Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu and Rafal Bogacz · 2020
Cited alongside, same era.
“Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks”
Wei Hu, Lechao Xiao and Jeffrey Pennington · 2020
Cited alongside, same era.
Francesco Innocenti et al · 2024
Later among the works it cites.
“Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?”
Francesco Innocenti, El Achour, Ryan Singh and Christopher Buckley · 2024
Later among the works it cites.
“Predictive coding networks for temporal prediction”
Beren Millidge et al · 2024
Later among the works it cites.
“Learning probability distributions of sensory inputs with Monte Carlo Predictive Coding”
Gaspard Oliviers, Rafal Bogacz and Alexander Meulemans · 2024
Later among the works it cites.
“Incremental Predictive Coding: A parallel and fully automatic learning algorithm”
Tommaso Salvatori et al · 2024
Later among the works it cites.
“Inferring neural activity before plasticity as a foundation for learning beyond backpropagation”
Yuhang Song et al · 2024
Later among the works it cites.
“Sample as you Infer: Predictive Coding with Langevin Dynamics”
Umais Zahid, Qinghai Guo and Zafeirios Fountas · 2024
Later among the works it cites.
“Understanding and Improving Optimization in Predictive Coding Networks”
Nicholas Alonso, Jeffrey Krichmar and Emre Neftci · 2024
Later among the works it cites.
“Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?”
Francesco Innocenti, El Achour, Ryan Singh and Christopher Buckley · 2024
Later among the works it cites.
“JPC: Flexible Inference for Predictive Coding Networks in JAX”
Francesco Innocenti et al · 2024
Later among the works it cites.
“Predictive coding networks for temporal prediction”
Beren Millidge et al · 2024
Later among the works it cites.
“Learning probability distributions of sensory inputs with Monte Carlo Predictive Coding”
Gaspard Oliviers, Rafal Bogacz and Alexander Meulemans · 2024
Later among the works it cites.
“Incremental Predictive Coding: A parallel and fully automatic learning algorithm”
Tommaso Salvatori et al · 2024
Later among the works it cites.
“Inferring neural activity before plasticity as a foundation for learning beyond backpropagation”
Yuhang Song et al · 2024
Later among the works it cites.
“Sample as you Infer: Predictive Coding with Langevin Dynamics”
Umais Zahid, Qinghai Guo and Zafeirios Fountas · 2024
Later among the works it cites.
“ μ \mu PC: Scaling Predictive Coding to 100+ Layer Networks”
Francesco Innocenti, El Achour and Christopher Buckley · 2025
Closest in time.
“Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding”
Kwangjun Lee, Cyriel.. Pennartz and Jorge. Mejias · 2025
Closest in time.
“Benchmarking Predictive Coding Networks – Made Simple”
Luca Pinchetti et al · 2025
Closest in time.
“Training Deep Predictive Coding Networks”
Chang Qi, Thomas Lukasiewicz and Tommaso Salvatori · 2025
Closest in time.
“ μ \mu PC: Scaling Predictive Coding to 100+ Layer Networks”
Francesco Innocenti, El Achour and Christopher Buckley · 2025
Closest in time.
“Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding”
Kwangjun Lee, Cyriel.. Pennartz and Jorge. Mejias · 2025
Closest in time.
“Benchmarking Predictive Coding Networks – Made Simple”
Luca Pinchetti et al · 2025
Closest in time.
“Training Deep Predictive Coding Networks”
Chang Qi, Thomas Lukasiewicz and Tommaso Salvatori · 2025
Closest in time.
“ μ \mu PC: Scaling Predictive Coding to 100+ Layer Networks”
Francesco Innocenti, El Achour and Christopher Buckley · 2025
Closest in time.
“Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding”
Kwangjun Lee, Cyriel.. Pennartz and Jorge. Mejias · 2025
Closest in time.
“Benchmarking Predictive Coding Networks – Made Simple”
Luca Pinchetti et al · 2025
Closest in time.
“Training Deep Predictive Coding Networks”
Chang Qi, Thomas Lukasiewicz and Tommaso Salvatori · 2025
Closest in time.
“Stable and Scalable Deep Predictive Coding Networks with Meta-Prediction Errors”
Myoung Ha et al · 2026
Closest in time.
“A survey on neuro-mimetic deep learning via predictive coding”
Tommaso Salvatori et al · 2026
Closest in time.
“Stable and Scalable Deep Predictive Coding Networks with Meta-Prediction Errors”
Myoung Ha et al · 2026
Closest in time.
“A survey on neuro-mimetic deep learning via predictive coding”
Tommaso Salvatori et al · 2026
Closest in time.
“Stable and Scalable Deep Predictive Coding Networks with Meta-Prediction Errors”
Myoung Ha et al · 2026
Closest in time.
“A survey on neuro-mimetic deep learning via predictive coding”
Tommaso Salvatori et al · 2026
Closest in time.