Enhancing Predictive Coding Networks for Multi-Modal Generation and Classification
Bibliographic record
Abstract
Predictive coding networks (PCnets) provide a biologically inspired framework for classification and generative tasks. However, their generative performance is limited when handling multi-modal class distributions, as the standard weight decay method struggles to differentiate between multiple modes within a class effectively. To address this issue, we propose two extensions: (1) integrating an auto-encoder (AE) to enhance latent representations and (2) employing a predictive coding Hopfield network (PCHN) to capture attractor dynamics. We evaluate these models on synthetic multi-Gaussian datasets and a subset of MNIST, revealing that the AE-enhanced PCnet can generate samples representing distinct clusters. Meanwhile, the PCHN variant independently discovers and maintains cluster structures without explicit latent supervision. Our findings highlight the potential of predictive coding as a sturdy framework for learning multi-modal data distributions in a biologically plausible manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".