The Home-Cage Automated Skilled Reaching Apparatus (HASRA): Individualized Training of Group-Housed Mice in a Single Pellet Reaching Task
Bibliographic record
Abstract
The single pellet reaching task is commonly used in rodents to assess the acquisition of fine motor skill and recovery of function following nervous system injury. Although this task is useful for gauging skilled forelimb use in rodents, the process of training animals is labor intensive and variable across studies and labs. To address these limitations, we developed a single pellet reaching paradigm for training and testing group housed mice within their home cage. Mice enter a training compartment attached to the outside of the cage and retrieve millet seeds presented on a motorized pedestal that can be individually positioned to present seeds to either forelimb. To identify optimal training parameters, we compared task participation and success rates between groups of animals that were presented seeds at two different heights (floor vs mouth height) and at different intervals (fixed-time vs trial-based). The mouth height/fixed interval presentation style was most effective at promoting reaching behavior as all mice reached for seeds within 5 d. Using this paradigm, we assessed stroke-induced deficits in home-cage reaching. Following three weeks of baseline training, reaching success rate was ∼40%, with most trials performed during the dark cycle. A forelimb motor cortex stroke significantly decreased interaction with presented seeds within the first 2 d and impaired reaching success rates for the first 7 d. Our data demonstrate that group-housed mice can be efficiently trained on a single pellet reaching task in the home cage and that this assay is sensitive to stroke induced motor impairments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".