Alpha rhythm subharmonics underlie responsiveness to theta burst stimulation via selective calcium plasticity
Bibliographic record
Abstract
Bayesian optimization is a promising method for this end.It allows to approximate noisy functions with only a limited number of samples.It allows efficient, adaptive sampling methods: Acquisition functions allow sampling in locations that are likely to give valuable insights.Thus, more information can be collected in a smaller number of samples.In this presentation, we illustrate how different variables such as the mphase of electroencephalogram (EEG) recordings, coil location, and orientation can be optimized.In this context, we demonstrate that the number of samples required to find a good target are substantially decreased using Bayesian Optimization than with random sampling.We also show how the algorithm convergence speed and accuracy is related to properties of the target variable, such as noisiness or dimensionality.We also give practical insights on how implementations of Bayesian optimization can be fine-tuned for different contexts.Specifically, we illustrate the trade-off between using a simple model for fast optimization and a complex model for higher target accuracy.We also address model complexity and computation time for multi-dimensional optimization.Overall, we show that Bayesian optimization is a suitable approach for closed-loop TMS experiments.We show different use-cases and illustrate which properties require modified implementations of the algorithm.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".