Temporal spacing of synaptic stimulation critically modulates the dependence of LTP on cyclic AMP‐dependent protein kinase
Bibliographic record
Abstract
Genetic and electrophysiological experiments have defined an important role for cAMP-dependent protein kinase (PKA) in certain forms of long-term potentiation (LTP). However, the characteristics of stimulation that are critical for regulating the PKA-dependence of LTP have not been clearly defined. In the present study, we have used PKA mutant mice (R(AB) transgenic mice), which have reduced PKA activity in neurons within the hippocampus, to explore the role of temporal spacing of synaptic stimulation in regulating the PKA-dependence of LTP. The time interval between successive bursts of electrical stimulation was varied while keeping constant the total number of stimulus pulses. LTP induced by temporally spaced tetraburst synaptic stimulation was impaired in the Schaeffer collateral pathway of hippocampal slices from R(AB) mutant mice. In contrast, LTP induced by temporally compressed tetraburst stimulation was normal in slices from R(AB) mutants, and its long-term maintenance was not significantly affected by bath application of KT-5720, an inhibitor of catalytic subunits of PKA. In slices from wildtype mice, LTP induced by spaced tetraburst stimulation was significantly attenuated by KT-5720. These genetic and pharmacological experiments show that LTP induced by these compressed patterns of stimulation does not require PKA activation. Thus, altering the temporal spacing of synaptic stimulation per se critically modulates the PKA-dependence of hippocampal LTP. PKA-dependent LTP is selectively recruited by temporally spaced, multiburst synaptic stimulation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".