Neuropeptide Y co-opts neuronal ensembles for memory lability and stability
Bibliographic record
Abstract
Abstract Memory engrams are formed by activity-dependent recruitment of distinct subsets of excitatory principal neurons (or neuronal ensembles) whereas inhibitory neurons pivot memory lability and stability 1–5 . However, the molecular logic for memory engrams to preferentially recruit specific type of interneurons over other subtypes remains enigmatic. Using activity-dependent single-cell transcriptomic profiling 6–8 in mice with training of cued fear memory and extinction, we discovered that neuropeptide Y (NPY)-expressing (NPY + ) GABAergic interneurons in the ventral hippocampal CA1 (vCA1) region exert fast GABAergic inhibition to facilitate the acquisition of memory, but bifurcate NPY-mediated slow peptidergic inhibition onto distinct sub-ensembles underlying the extinction of single memory trace. Genetically encoded calcium and NPY sensors revealed that both calcium dynamics of NPY + neurons and their NPY release in vCA1 ramp up as extinction learning progresses while behavioral state switches from “fear-on” to “fear-off”. Bidirectional manipulations of NPY + neurons or NPY itself demonstrated NPY is both necessary and sufficient to control the rate and degree of memory extinction by acting on two physically non-overlapping sub-ensembles composed of NPY1R- and NPY2R-expressing neurons. CRISPR/Cas9-mediated knockout of NPY2R or NPY1R further unravels that NPY co-opts its actions on these two sub-ensembles to gate early fast and late slow stages of extinction. These findings exemplify the intricate spatiotemporal orchestration of slow peptidergic inhibitions from single subtype of GABAergic interneurons to fine-tune engram lability verse stability of memory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".