Pre- and postsynaptic N-methyl-D-aspartate receptors are required for sequential printing of fear memory engrams
Bibliographic record
Abstract
The organization of fear memory involves the participation of multiple brain regions. However, it is largely unknown how fear memory is formed, which circuit pathways are used for "printing" memory engrams across brain regions, and the role of identified brain circuits in memory retrieval. With advanced genetic methods, we combinatorially blocked presynaptic output and manipulated N-methyl-D-aspartate receptor (NMDAR) in the basolateral amygdala (BLA) and medial prefrontal cortex (mPFC) before and after cued fear conditioning. Further, we tagged fear-activated neurons during associative learning for optogenetic memory recall. We found that presynaptic mPFC and postsynaptic BLA NMDARs are required for fear memory formation, but not expression. Our results provide strong evidence that NMDAR-dependent synaptic plasticity drives multi-trace systems consolidation for the sequential printing of fear memory engrams from BLA to mPFC and, subsequently, to the other regions, for flexible memory retrieval.
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".