Bibliographic record
Abstract
Resolving how our brains encode information requires an understanding of the cellular processes taking place during memory formation. Since the 1970s, considerable effort has focused on determining the properties and mechanisms underlying long-term potentiation (LTP) at glutamatergic synapses and how these processes influence initiation of new memories. However, accumulating evidence suggests that long-term depression (LTD) of synaptic strength, particularly at glutamatergic synapses, is a bona fide learning and memory mechanism in the mammalian brain. The known range of mechanisms capable of inducing LTD has been extended to those including NMDAR-independent forms, neuromodulator-dependent LTD, synaptic depression following stress, and non-synaptically induced forms. The examples of LTD observed at the hippocampal CA1 synapse to date demonstrate features consistent with LTP, including homo- and heterosynaptic expression, extended duration beyond induction (several hours to weeks), and association with encoding of distinct types of memories. Canonical mechanisms through which synapses undergo LTD include activation of phosphatases, initiation of protein synthesis, and dynamic regulation of presynaptic glutamate release and/or postsynaptic glutamate receptor endocytosis. Here, we will discuss the pre- and postsynaptic changes underlying LTD, recent advances in the identification and characterization of novel mechanisms underlying LTD, and how engagement of these processes constitutes a cellular analog for the genesis of specific types of memories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.231 | 0.167 |
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".