Engram stability and maturation during systems consolidation
Bibliographic record
Abstract
Remote memories play an important role in how we perceive the world, and they are rooted throughout the brain in "engrams": ensembles of cells that are formed during acquisition. Upon their reactivation, a specific memory can be recalled. 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 Many studies have focused on the ensembles in CA1 of the hippocampus and the anterior cingulate cortex (ACC). However, the evolution of these components during systems' consolidation has not yet been comprehensively addressed. 13 , 14 , 15 , 16 By applying transgenic approaches for ensemble identification, CLARITY, retro-AAV, and pseudo-rabies virus for circuit mapping, and chemogenetics for functional interrogation, we addressed the dynamics of recent and remote CA1 ensembles. We expected both stability (as they represent the same memory) and maturation (over time). Indeed, we found that CA1 engrams remain stable between recent and remote recalls, and the inhibition of engrams for recent recall during remote recall functionally impairs memory. We also found that new cells in the remote recall engram in the CA1 are not added randomly during maturation but differ according to their connections. First, we show in two ways that the anterograde CA1 → ACC engram cell projection grows larger. Finally, in the retrograde projections, the ACC reduces input to CA1 engram cells, whereas input from the entorhinal cortex and paraventricular nucleus of the thalamus increases. Our results shine fresh light on systems' consolidation by providing a deeper understanding of engram stability and maturation in the transition from recent to remote memory.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".