Adaptive optimal-control algorithms for brainlike networks.
Bibliographic record
Abstract
Many neural control systems are at least roughly optimized, but how is optimal control \nlearned in the brain? There are algorithms for this purpose, but in their present forms they aren’t suited for biological neural networks because they rely on a type of communication that isn’t available in the brain, namely weight transport — transmitting the strengths, or “weights”, of individual synapses to other synapses and neurons. Here I show how optimal control can be learned without weight transport. I explore three complementary approaches. In the first, I show that the control-theory \nconcept of feedback linearization can form the basis for a simple mechanism that learns \nroughly optimal control, at least in some sensorimotor tasks. Second, I describe a method based on Pontryagin’s Minimum Principle of optimal control, by which a network without weight transport might achieve optimal open-loop control. Third, I describe a mechanism for building optimal feedback controllers, without weight transport, by a method based on generalized Hamilton-Jacobi-Bellman equations. Finally, I argue that the issues raised in these three projects apply quite broadly, i.e. most control algorithms rely on weight transport in many different ways, but it may be possible to recast them into forms that are free of such transport by the mechanisms I propose.
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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) | 0.002 | 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".