Manipulating a “Cocaine Engram” in Mice
Bibliographic record
Abstract
Experience with drugs of abuse (such as cocaine) produces powerful, long-lasting memories that may be important in the development and persistence of drug addiction. The neural mechanisms that mediate how and where these cocaine memories are encoded, consolidated and stored are unknown. Here we used conditioned place preference in mice to examine the precise neural circuits that support the memory of a cocaine-cue association (the "cocaine memory trace" or "cocaine engram"). We found that a small population of neurons (∼10%) in the lateral nucleus of amygdala (LA) were recruited at the time of cocaine-conditioning to become part of this cocaine engram. Neurons with increased levels of the transcription factor CREB were preferentially recruited or allocated to the cocaine engram. Ablating or silencing neurons overexpressing CREB (but not a similar number of random LA neurons) before testing disrupted the expression of a previously acquired cocaine memory, suggesting that neurons overexpressing CREB become a critical hub in what is likely a larger cocaine memory engram. Consistent with theories that coordinated postencoding reactivation of neurons within an engram or cell assembly is crucial for memory consolidation (Marr, 1971; Buzsáki, 1989; Wilson and McNaughton, 1994; McClelland et al., 1995; Girardeau et al., 2009; Dupret et al., 2010; Carr et al., 2011), we also found that post-training suppression, or nondiscriminate activation, of CREB overexpressing neurons impaired consolidation of the cocaine memory. These findings reveal mechanisms underlying how and where drug memories are encoded and stored in the brain and may also inform the development of treatments for drug addiction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".