Ensembles and engrams in mouse cortical and sub-thalamic brain regions supporting context and memory recall
Bibliographic record
Abstract
Associative learning supports learning about outcomes associated with contexts and cues. During learning, cellular ensembles that become active can be incorporated into a memory engram and later reactivated to support memory recall. Studies exploring engram formation and reactivation have primarily used contextual conditioning in mice and made little distinction between engrams encoding contextual information versus cue-associated learning and recall. Furthermore, often missing in such analyses is exploration of sex differences in engram profiles. Using auditory fear conditioning and activity-dependent tagging in mice, we set out to disaggregate context-associated engrams from those associated with learning and recall while also profiling potential sex differences. Specifically, we quantified cellular activity during context exposure, fear recall, extinction training, and extinction recall in cortical and subthalamic brain regions supporting learning and memory. We found that male mice had larger ensembles of cells active in the infralimbic prefrontal cortex (IL-PFC) during context exposure while female mice recalling a fear memory had a significantly greater proportion of cells that were active in the IL-PFC independent of context. Across sexes, we found greater reactivation of extinction engrams in the IL-PFC compared to contextual engrams. While we found ensembles and engrams in the prelimbic prefrontal cortex (PL-PFC) and zona incerta (ZI), no sex differences were noted in these regions. These results not only emphasize that there is a distinction to be made between ensembles and engrams encoding contextual information from those encoding cue-associated learning and recall, but also highlight sex differences in ensemble and engram allocation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".