Salient Experiences are Represented by Unique Transcriptional Signatures in the Brain
Bibliographic record
Abstract
Summary Inducible transcription is essential for consolidation of salient experiences into long-term memory. However, the question of whether inducible transcription relays information representing the identity of the experience being encoded, has not been explored. To this end, we have analyzed transcription across multiple brain regions, induced by a variety of rewarding and aversive experiences. Our results define robust transcriptional signatures uniquely characterizing individual salient experiences. A subset of these induced transcriptional markers suffice for near-perfect decoding of the identity of recent experiences at the level of individual mice. Furthermore, experiences with shared attributes display commonalities in their transcriptional representation, exemplified in the representation of valence, habituation and reinforcement. Taken together, our results demonstrate the existence of a neural transcriptional code that represents the encoding of experiences in the mouse brain. This code is comprised of distinct transcriptional signatures that correlate to the affective attributes of the experiences that are being encoded.
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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 | 0.000 |
| 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".