Cold memories control whole-body thermoregulatory responses
Bibliographic record
Abstract
Environmental thermal challenges trigger the brain to coordinate both autonomic and behavioural responses to maintain optimal body temperature1–4. It is unknown how temperature information is precisely stored and retrieved in the brain and how it is converted into a physiological response. Here we investigated whether memories could control whole-body metabolism by training mice to remember a thermal challenge. Mice were conditioned to associate a context with a specific temperature by combining thermoregulatory Pavlovian conditioning with engram-labelling technology, optogenetics and chemogenetics. We report that if mice are returned to an environment in which they previously experienced a 4 °C cold challenge, they increase their metabolic rates regardless of the actual environmental temperature. Furthermore, we show that mice have increased hypothalamic activity when they are exposed to the cold, and that a specific network emerges between the hippocampus and the hypothalamus during the recall of a cold memory. Both natural retrieval and artificial reactivation of cold-sensitive memory engrams in the hippocampus mimic the physiological responses that are seen during a cold challenge. These ensembles are necessary for cold-memory retrieval. These findings show that retrieval of a cold memory causes whole-body autonomic and behavioural responses that enable mice to maintain thermal homeostasis. Cold-sensitive engrams contribute to learned thermoregulation in mice that are returned to an environment in which they previously experienced a cold challenge, through a network formed between the hippocampus and hypothalamus that enables the recall of cold-related memories.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".