Assessing spatial learning and memory in mice: Classic radial maze versus a new animal-friendly automated radial maze allowing free access and not requiring food deprivation
Bibliographic record
Abstract
The radial arm maze (RAM) is a common behavioral test to quantify spatial learning and memory in rodents. Prior attempts to refine the standard experimental setup have been insufficient. Previously, we demonstrated the feasibility of a fully automated, voluntary, and stress-free eight-arm RAM not requiring food or water deprivation. Here, we compared this newly developed refined RAM to a classic manual experimental setup using 24 female 10-12 weeks old C57BL/6J mice. We used a lipopolysaccharide (LPS)-induced model of systemic inflammation to examine long-term cognitive impairment for up to 13 weeks following LPS injection. Both mazes demonstrated robust spatial learning performance during the working memory paradigm. The refined RAM detected spatial learning and memory deficits among LPS-treated mice in the working memory paradigm, whereas the classic RAM detected spatial learning and memory deficits only in the combined working/reference memory paradigm. In addition, the refined RAM allowed for quantification of an animal's overall exploratory behavior and day/night activity pattern. While our study highlights important aspects of refinement of the new setup, our comparison of methods suggests that both RAMs have their respective merits depending on experimental requirements.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".