Short‐term plasticity impacts information transfer at glutamate synapses onto parvocellular neuroendocrine cells in the paraventricular nucleus of the hypothalamus
Bibliographic record
Abstract
Glutamatergic synaptic inputs onto parvocellular neurosecretory cells (PNCs) in the paraventricular nucleus of the hypothalamus (PVN) regulate the hypothalamic-pituitary-adrenal (HPA) axis responses to stress and undergo stress-dependent changes in their capacity to transmit information. In spite of their pivotal role in regulating PNCs, relatively little is known about the fundamental rules that govern transmission at these synapses. Furthermore, since salient information in the nervous system is often transmitted in bursts, it is also important to understand the short-term dynamics of glutamate transmission under basal conditions. To characterize these properties, we obtained whole-cell patch clamp recordings from PNCs in brain slices from postnatal day 21-35 male Sprague-Dawley rats and examined EPSCs. EPSCs were elicited by electrically stimulating glutamatergic afferents along the periventricular aspect. In response to a paired-pulse stimulation protocol, EPSCs generally displayed a robust short-term depression that recovered within 5 s. Similarly, trains of synaptic stimuli (5-50 Hz) resulted in a frequency-dependent depression until a near steady state was achieved. Application of inhibitors of AMPA receptor (AMPAR) desensitization or the low-affinity, competitive AMPAR antagonist failed to affect the depression due to paired-pulse and trains of synaptic stimulation indicating that this use-dependent short-term synaptic depression has a presynaptic locus of expression. We used cumulative amplitude profiles during trains of stimulation and variance-mean analysis to estimate synaptic parameters. Finally, we report that these properties contribute to hamper the efficiency with which high frequency synaptic inputs generate spikes in PNCs, indicating that these synapses operate as effective low-pass filters in basal conditions.
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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".