Predictive Coding Graphs are a Superset of Feedforward Neural Networks
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912948059897856 |
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| author | van Zwol, Björn |
| author_facet | van Zwol, Björn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06142 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predictive Coding Graphs are a Superset of Feedforward Neural Networks van Zwol, Björn Machine Learning Disordered Systems and Neural Networks Artificial Intelligence Neural and Evolutionary Computing 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. |
| title | Predictive Coding Graphs are a Superset of Feedforward Neural Networks |
| topic | Machine Learning Disordered Systems and Neural Networks Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2603.06142 |