Multiple Serotonergic Mechanisms Contributing to Sensitization in<i>Aplysia</i>: Evidence of Diverse Serotonin Receptor Subtypes
Bibliographic record
Abstract
The neurotransmitter serotonin (5-HT) plays an important role in memory encoding in Aplysia. Early evidence showed that during sensitization, 5-HT activates a cyclic AMP-protein kinase A (cAMP-PKA)-dependent pathway within specific sensory neurons (SNs), which increases their excitability and facilitates synaptic transmission onto their follower motor neurons (MNs). However, recent data suggest that serotonergic modulation during sensitization is more complex and diverse. The neuronal circuits mediating defensive reflexes contain a number of interneurons that respond to 5-HT in ways opposite to those of the SNs, showing a decrease in excitability and/or synaptic depression. Moreover, in addition to acting through a cAMP-PKA pathway within SNs, 5-HT is also capable of activating a variety of other protein kinases such as protein kinase C, extracellular signal-regulated kinases, and tyrosine kinases. This diversity of 5-HT responses during sensitization suggests the presence of multiple 5-HT receptor subtypes within the Aplysia central nervous system. Four 5-HT receptors have been cloned and characterized to date. Although several others probably remain to be characterized in molecular terms, especially the Gs-coupled 5-HT receptor capable of activating cAMP-PKA pathways, the multiplicity of serotonergic mechanisms recruited into action during learning in Aplysia can now be addressed from a molecular point of view.
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 | 0.000 | 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".