Comparing behaviours induced by natural memory retrieval and optogenetic reactivation of an engram ensemble in mice
Bibliographic record
Abstract
Memories are thought to be stored within sparse collections of neurons known as engram ensembles. Neurons active during a training episode are allocated to an engram ensemble ('engram neurons'). Memory retrieval is initiated by external sensory or internal cues present at the time of training reactivating engram neurons. Interestingly, optogenetic reactivation of engram ensemble neurons alone in the absence of external sensory cues is sufficient to induce behaviour consistent with memory retrieval in mice. However, there may exist differences between the behaviours induced by natural retrieval cues or artificial engram reactivation. Here, we compared two defensive behaviours (freezing and the syllable structure of ultrasonic vocalizations, USVs) induced by sensory cues present at training (natural memory retrieval) and optogenetic engram ensemble reactivation (artificial memory retrieval) in a threat conditioning paradigm in the same mice. During natural memory recall, we observed a strong positive correlation between freezing levels and distinct USV syllable features (characterized by an unsupervised algorithm, MUPET (Mouse Ultrasonic Profile ExTraction)). Moreover, we observed strikingly similar behavioural profiles in terms of freezing and USV characteristics between natural memory recall and artificial memory recall in the absence of sensory retrieval cues. Although our analysis focused on two behavioural measures of threat memory (freezing and USV characteristics), these results underscore the similarities between threat memory recall triggered naturally and through optogenetic reactivation of engram ensembles. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.
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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 | 0.001 |
| 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) | 0.001 | 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".