Protein Synthesis Is Required for Synaptic Immunity to Depotentiation
Bibliographic record
Abstract
De novo protein synthesis and transcription are necessary for the expression of long-lasting synaptic potentiation [long-term potentiation (LTP)] in hippocampal area CA1 and for the consolidation of long-term memory. The stability of LTP and its longevity require macromolecular synthesis at later stages, but a specific role for early protein synthesis has not been identified. Using electrophysiological recording methods in mouse hippocampal slices, we show that multiple trains of high-frequency stimulation provide immediate synaptic immunity to depotentiation. This immunity to depotentiation is dependent on the amount of synaptic stimulation used to induce LTP, it is input specific, and it is prevented by inhibitors of protein synthesis. We propose that local translation mediates input-specific synaptic immunity against depotentiation. We also present evidence suggesting that, in addition to translation, products of transcription can provide cell-wide immunity to depotentiation via heterosynaptic transfer of synaptic immunity between distinct pathways in area CA1. Protein synthesis and transcription may importantly regulate long-term storage of information by conferring synaptic immunity to depotentiation at previously potentiated synapses.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".