Examining memory linking and generalization using scFLARE2, a temporally precise neuronal activity tagging system
Bibliographic record
Abstract
How memories are organized in the brain influences whether they are remembered discretely versus linked with other experiences or whether generalized information is applied to entirely novel situations. Here, we used scFLARE2 (single-chain fast light- and activity-regulated expression 2), a temporally precise tagging system, to manipulate mouse lateral amygdala neurons active during one of two 3 min threat experiences occurring close (3 h) or further apart (27 h) in time. Silencing scFLARE2-tagged neurons showed that two threat experiences occurring at distal times are dis-allocated to orthogonal engram ensembles and remembered discretely, whereas the same two threat experiences occurring in close temporal proximity are linked via co-allocation to overlapping engram ensembles. Moreover, we found that co-allocation mediates memory generalization applied to a completely novel stimulus. These results indicate that endogenous temporal evolution of engram ensemble neuronal excitability determines how memories are organized and remembered and that this would not be possible using conventional immediate-early gene-based tagging methods.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".