Food Anticipatory Activity Behavior of Mice across a Wide Range of Circadian and Non-Circadian Intervals
Bibliographic record
Abstract
When rodents are fed in a limited amount during the daytime, they rapidly redistribute some of their nocturnal activity to the time preceding the delivery of food. In rats, anticipation of a daily meal has been interpreted as a circadian rhythm controlled by a food-entrained oscillator (FEO) with circadian limits to entrainment. Lesion experiments place this FEO outside of the light-entrainable circadian pacemaker in the suprachiasmatic nucleus. Mice also anticipate a fixed daily meal, but circadian limits to entrainment and anticipation of more than 2 daily meals, have not been assessed. We used a video-based behavior recognition system to quantify food anticipatory activity in mice receiving 2, 3, or 6 daily meals at intervals of 12, 8, or 4-hours (h). Individual mice were able to anticipate as many as 4 of 6 daily meals, and anticipation persisted during meal omission tests. On the 6 meal schedule, pre-prandial activity and body temperature were poorly correlated, suggesting independent regulation. Mice showed a limited ability to anticipate an 18 h feeding schedule. Finally, mice showed concurrent circadian and sub-hourly anticipation when provided with 6 small meals, at 30 minute intervals, at a fixed time of day. These results indicate that mice can anticipate feeding opportunities at a fixed time of day across a wide range of intervals not previously associated with anticipatory behavior in studies of rats. The methods described here can be exploited to determine the extent to which timing of different intervals in mice relies on common or distinct neural and molecular mechanisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".