The role of the genome in experience-dependent plasticity: Extending the analogy of the genomic action potential
Bibliographic record
Abstract
Our past experiences shape our current and future behavior. These experiences must leave some enduring imprint on our brains, altering neural circuits that mediate behavior and contributing to our individual differences. As a framework for understanding how experiences might produce lasting changes in neural circuits, Clayton [D. F. Clayton, Neurobiol. Learn. Mem. 74, 185–216 (2000)] introduced the concept of the genomic action potential (gAP)—a structured genomic response in the brain to acute experience. Similar to the familiar electrophysiological action potential (eAP), the gAP also provides a means for integrating afferent patterns of activity but on a slower timescale and with longer-lasting effects. We revisit this concept in light of contemporary work on experience-dependent modification of neural circuits. We review the “Immediate Early Gene” (IEG) response, the starting point for understanding the gAP. We discuss evidence for its involvement in the encoding of experience to long-term memory across time and biological levels of organization ranging from individual cells to cell ensembles and whole organisms. We explore distinctions between memory encoding and homeostatic functions and consider the potential for perpetuation of the imprint of experience through epigenetic mechanisms. We describe a specific example of a gAP in humans linked to individual differences in the response to stress. Finally, we identify key objectives and new tools for continuing research in this area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".