Low‐frequency‐induced synaptic potentiation: A paradigm shift in the field of memory‐related plasticity mechanisms?
Bibliographic record
Abstract
Long-term potentiation (LTP) and long-term depression (LTD) are two forms of synaptic plasticity thought to play functional roles in learning and memory processes. It is generally assumed that the direction of synaptic modifications (i.e., up- or down-regulation of synaptic strength) depends on the specific pattern of afferent inputs, with high frequency activity or stimulation effectively inducing LTP, while low-frequency patterns often elicit LTD. This dogma ("high frequency-LTP, low frequency-LTD") has recently been challenged by evidence demonstrating low frequency stimulation (LFS)-induced synaptic potentiation in the rodent hippocampus and amygdala. Extensive work in the past decades has focused on deciphering the mechanisms by which high frequency stimulation of afferent fiber systems results in LTP. With this review, we will compare and contrast the well-known synaptic and cellular mechanisms underlying classical, high-frequency-induced LTP to those mediating the more recently discovered phenomena of LFS-induced synaptic enhancement. In addition, we argue that LFS protocols provide a means to more accurately mimic some endogenous, oscillatory activity patterns present in hippocampal and extra-hippocampal (especially neocortical) circuits during periods of memory consolidation. Consequently, LFS-induced synaptic potentiation offers a novel and important avenue to investigate cellular and systems-level mechanisms mediating the encoding, consolidation, and transfer of information throughout multiple forebrain networks implicated in learning and memory processes. (c) 2009 Wiley-Liss, Inc.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".