Synapse Number and Synaptic Efficacy Are Regulated by Presynaptic cAMP and Protein Kinase A
Bibliographic record
Abstract
The mechanisms by which neurons regulate the number and strength of synapses during development and synaptic plasticity have not yet been defined fully. This lack of fundamental knowledge in the fields of neurodevelopment and synaptic plasticity can be attributed, in part, to compensatory mechanisms by which neurons accommodate for the loss of function in their synaptic partners. This is generally achieved either by scaling up neuronal transmitter release capabilities or by enhancing the postsynaptic responsiveness. Here, we demonstrate that regulation of synaptic strength and number between identified Lymnaea neurons visceral dorsal 4 (VD4, the presynaptic cell) and left pedal dorsal 1 (LPeD1, the postsynaptic cell) requires presynaptic activation of a cAMP-PKA-dependent signal. Experimental activation of the cAMP-PKA pathway resulted in reduced synaptic efficacy, whereas inhibition of the cAMP-PKA cascade permitted hyperinnervation and an overall enhancement of synaptic strength. Because synaptic transmission between VD4 and LPeD1 does not require a cAMP-PKA pathway, our data show that these messengers may play a novel role in regulating the synaptic efficacy during early synaptogenesis and plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".