Caractérisation d'une tâche de préhension automatisée supportant l'enregistrement physiologique et les traitements neuromodulatoires chez le rat
Bibliographic record
Abstract
RÉSUMÉ: Ce mémoire présente le développement et l’optimisation d’une plateforme robotisée appelée NeuroRobotic Platform, conçue pour étudier les mouvements de préhension fine et la récu-pération fonctionnelle après des lésions neurologiques. Réalisé au laboratoire du Dr Numa Dancause à l’Université de Montréal, ce projet a pour objectif principal de fournir un dis-positif capable de recueillir des données précises sur les performances motrices des rats tout en automatisant et standardisant les protocoles expérimentaux, en réponse aux limites des outils traditionnels, souvent chronophages et dépendants de l’intervention humaine. La plateforme répond à deux objectifs spécifiques. Le premier consiste à optimiser son fonc-tionnement technique, incluant l’ajustement des capteurs, la détection automatisée des essais réussis et échoués, et la modulation dynamique de la difficulté des tâches par un positionne-ment précis des récompenses dans un espace tridimensionnel accessible aux rats. Le second vise à démontrer la capacité de la plateforme à collecter des données détaillées et synchronisées sur les performances motrices, à mettre en œuvre des approches progressives d’apprentissage des tâches, et à enregistrer des signaux musculaires provenant du membre supérieur des rats pendant leur exécution. ABSTRACT: This thesis presents the development and optimization of a robotic platform called the Neu-roRobotic Platform, designed to study fine grasping movements and functional recovery following neurological lesions. Conducted in Dr. Numa Dancause’s laboratory at the Univer-sity of Montreal, the primary goal of this project is to provide a device capable of collecting precise data on rats’ motor performance while automating and standardizing experimental protocols to address the limitations of traditional tools, which are often time-consuming and reliant on human intervention. The platform meets two specific objectives. The first focuses on optimizing its technical performance, including sensor adjustments, automated detection of successful and failed at-tempts, and dynamic modulation of task difficulty through precise positioning of rewards in a three-dimensional space accessible to the rats. The second objective aims to demonstrate the platform’s ability to collect detailed and synchronized data on motor performance, imple-ment progressive task-learning protocols, and record muscle signals from the rats’ forelimbs during task execution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".