Introduction to Predictive Coding Networks for Machine Learning
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Abstract (arXiv)
Predictive coding networks (PCNs) constitute a biologically inspired framework for understanding hierarchical computation in the brain, and offer an alternative to traditional feedforward neural networks in ML. This note serves as a quick, onboarding introduction to PCNs for machine learning practitioners. We cover the foundational network architecture, inference and learning update rules, and algorithmic implementation. A concrete image-classification task (CIFAR-10) is provided as a benchmark-smashing application, together with an accompanying Python notebook containing the PyTorch implementation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 31 May 2025.
Builds on
9 of 31 references.
References · 9
- 2018World Modelscited by 1,444
- 2016Deep Predictive Coding Networks for Video Prediction and Unsupervised Learningcited by 203
- 2020Predictive Coding Approximates Backprop along Arbitrary Computation Graphscited by 89
- 2021Predictive Coding: a Theoretical and Experimental Reviewcited by 74
- 2022Hybrid Predictive Coding: Inferring, Fast and Slowcited by 25
- 2022A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networkscited by 21
- 2022A Theoretical Framework for Inference Learningcited by 17
- 2024Tight Stability, Convergence, and Robustness Bounds for Predictive Coding Networkscited by 3
- 2024Predictive Coding with Spiking Neural Networks: a Surveycited by 2
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Citing papers per year: 2026: 2.
2 papers.