Predictive Coding Graphs are a Superset of Feedforward Neural Networks
Machine Learning
2026-03-09 v1 Disordered Systems and Neural Networks
Artificial Intelligence
Neural and Evolutionary Computing
Machine Learning
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.
Cite
@article{arxiv.2603.06142,
title = {Predictive Coding Graphs are a Superset of Feedforward Neural Networks},
author = {Björn van Zwol},
journal= {arXiv preprint arXiv:2603.06142},
year = {2026}
}
Comments
11 pages, 3 figures. Accepted at the NeuroAI Workshop @ NeurIPS 2024. OpenReview: https://openreview.net/forum?id=J36z3R0sNq