Adaptive Regulation: Modelling Cerebellum-driven Processes
Bibliographic record
Abstract
Understanding the function of the cerebellum remains an open problem of systems neuroscience. The thesis adopts a recent hypothesis that characterizes the cerebellum as an adaptive internal model, with the goal of rejecting disturbances acting on various regulated subsystems. The main challenge in utilizing regulator theory to explain cerebellar functions arises from its traditional development being primarily focused on engineering applications, which presents limitations in its application for brain modeling. The thesis aims to advance regulator theory and make it more suitable for systems neuroscience applications by addressing two main obstacles. The first obstacle regards the fact that traditional optimal control theory is concerned with optimizing transient behavior making it not suitable for capturing the brain’s optimization of its steady-state operation. To bridge this gap, we formulate the optimal steady-state regulation (OSSR) problem, which incorporates a cost on maintaining steady-state inputs and outputs of controllers contributing to regulation. We develop a fully adaptive control architecture to solve a specific instance of the OSSR problem involving two control modules: an inexpensive state feedback and a costly adaptive internal model. The design is used to realize a model of long-term adaptation of the brainstem neural integrator motor command in the oculomotorsystem, successfully recovering several experimental findings. The second obstacle addressed by the thesis regards intermittency of measurements which leads to a switched system with multiple equilibria. We extend the methods in the literature to provide the first stability results for discrete-time switched systems with multiple equilibria that do not impose any dwell-time constraint on the switching signal. These results are applied to show the stability ofa model of visuomotor adaptation under intermittent visual error measurement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".