Predictive Coding Graphs are a Superset of Feedforward Neural Networks
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Abstract (arXiv)
Predictive coding graphs (PCGs) are a recently introduced generalization to predictive coding networks, a neuroscience-inspired probabilistic latent variable model. Here, we prove how PCGs define a mathematical superset of feedforward artificial neural networks (multilayer perceptrons). This positions PCNs more strongly within contemporary machine learning (ML), and reinforces earlier proposals to study the use of non-hierarchical neural networks for ML tasks, and more generally the notion of topology in neural networks.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 6 Mar 2026.
Builds on
11 of 24 references.
References · 11
- 2016Wide Residual Networkscited by 3,057
- 2020The Neural Coding Framework for Learning Generative Modelscited by 44
- 2022Learning on Arbitrary Graph Topologies via Predictive Codingcited by 33
- 2023Brain-inspired Computational Intelligence via Predictive Codingcited by 30
- 2024Benchmarking Predictive Coding Networks -- Made Simplecited by 29
- 2021Reverse Differentiation via Predictive Codingcited by 23
- 2020Predictive Coding, Variational Autoencoders, and Biological Connectionscited by 21
- 2022A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networkscited by 21
- 2021On the relationship between predictive coding and backpropagationcited by 20
- 2022Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchycited by 17
- 2024Predictive Coding Networks and Inference Learning: Tutorial and Surveycited by 7
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Citing papers per year: 2026: 1.
1 paper.