Closed-Loop Estimation Method of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization
Bibliographic record
Abstract
BACKGROUND: The existing estimation methods of strength-duration (SD) curve are based on open-loop uniform and/or random pulse durations, which are chosen without feedback from neuronal data. OBJECTIVE: To develop a closed-loop estimation method of the SD curve, where the pulse durations are adjusted iteratively using the neuronal data. METHOD: In the proposed method, after the selection of each pulse duration through Fisher information matrix (FIM) optimization, the corresponding motor threshold (MT) is computed, the SD curve estimation is updated, and the process continues until satisfaction of a stopping rule. RESULTS: 250 simulation cases were run, and the results were compared with the iterative random and uniform sampling methods. The FIM method satisfied the stopping rule in 90% runs and estimated the rheobase (chronaxie in parenthesis) with an average absolute relative error (ARE) of 1.57% (2.15%), with an average of 85 samples. At the FIM termination sample, methods with two and all random pulse durations, and uniform methods with descending, ascending and random orders led to 5.69% (20.09%), 2.22% (3.93%), 7.34% (40.90%), 3.10% (4.44%), and 2.05% (3.45%) AREs. CONCLUSIONS: The FIM method proposes the SD identification by fitting to the data of the minimum and maximum pulse durations. The range of pulse duration should cover the vertical and horizontal parts of the SD curve. Iterative random or uniform samples from only the vertical or horizontal areas of the curve might not result in satisfactory estimation. SIGNIFICANCE: This paper provides insights about pulse durations selection for SD curve estimation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".