CREB, Synapses and Memory Disorders: Past Progress and Future Challenges
Bibliographic record
Abstract
In neurons, appropriate long-term adaptive responses to changes in the environment require the conversion of extracellular stimuli into discrete intracellular signals. Many of these signals involve the regulation of gene expression. The cAMP responsive element binding protein (CREB) is a nuclear transcription factor that modulates transcription of genes containing cAMP responsive elements (CRE sites) in their promoters. CREB is a key part of many intracellular signaling events that critically regulate many neural functions. Numerous studies on invertebrates and vertebrates demonstrate that CREB is critical for long-term memory. Here, we review the key features of CREB-dependent transcription and critically evaluate the data examining the roles of CREB in different forms of plasticity, including long-term memory in mammals. Because learning and memory have been linked to specific types of synaptic plasticity in several species, we also review studies on the role of CREB in long-term facilitation in Aplysia and in hippocampal long-term potentiation (LTP). Several human cognitive disorders have been linked to alterations of CREB-regulated gene expression. Therefore, we explore the possibility of targeting CREB function in developing novel treatment strategies. Finally, we highlight areas of research on CREB that are ripe for further advancement.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".