Closed-form predictive coding via hierarchical Gaussian filters
A new paper presents a method for closed-form predictive coding using hierarchical Gaussian filters. This approach addresses the limitations of current predictive coding networks by incorporating precision-weighted message passing. The proposed method shows improved performance on various tasks compared to traditional backpropagation techniques.
- ▪Predictive coding offers a biologically grounded alternative to backpropagation in training neural networks.
- ▪The new method restores precision-weighted message passing, yielding dynamic uncertainty estimates.
- ▪The approach outperforms traditional methods on online, data efficiency, and concept-drift tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20293 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.20293 (cs) [Submitted on 19 May 2026] Title:Closed-form predictive coding via hierarchical Gaussian filters Authors:Aleksandrs Baskakovs, Sylvain Estebe, Kenneth Enevoldsen, Kristoffer Nielbo, Chris Mathys, Nicolas Legrand View a PDF of the paper titled Closed-form predictive coding via hierarchical Gaussian filters, by Aleksandrs Baskakovs and Sylvain Estebe and Kenneth Enevoldsen and Kristoffer Nielbo and Chris Mathys and Nicolas Legrand View PDF Abstract:Predictive coding (PC) offers a local and biologically grounded alternative to backpropagation in the training of artificial neural networks, yet to date, it remains slower, and performance degrades sharply as network depth increases.
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