Predictive Coding Graphs are a Superset of Feedforward Neural Networks
Abstract
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.
Fields of Study & Subjects
Neural Networks and Applications
Bayesian Modeling and Causal Inference
Neural dynamics and brain function
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 6 March 2026
- Language
- EN
- Document type
- Preprint