Retrograde Regulation of GABA Transmission by the Tonic Release of Oxytocin and Endocannabinoids Governs Postsynaptic Firing
Bibliographic record
Abstract
The probability of neurotransmitter release at the nerve terminal is an important determinant of synaptic efficacy. At some central synapses, the postsynaptic, or target, neuron determines neurotransmitter release probability (P(r)) at the presynaptic terminal. The mechanisms responsible for this target-cell dependent control of P(r) have not been elucidated. Using whole-cell patch-clamp recordings from magnocellular neurosecretory cells in the paraventricular and supraoptic nuclei of the hypothalamus, we demonstrate that inhibitory, GABA synapses specifically onto oxytocin (OT)-producing neurosecretory cells exhibit a low P(r) that is relatively uniform at multiple synapses onto the same cell. This low P(r) results from a two-step process that requires the tonic release of OT from the postsynaptic cell. The ambient extracellular levels of neuropeptide are sufficient to activate postsynaptic OT receptors and trigger the Ca2+-dependent production of endocannabinoids, which act in a retrograde manner at presynaptic cannabinoid CB1 receptors to decrease GABA release. The functional consequence of this tonic inhibition of GABA release is that all inhibitory inputs facilitate uniformly when activated at high rates of activity. This causes inhibition in the postsynaptic cell that is sufficiently powerful to disrupt firing. Blockade of CB1 receptors increases P(r) at these synapses, resulting in a rapid depression of IPSCs at high rates of activity, thereby eliminating the ability of afferent inputs to inhibit postsynaptic firing. By playing a deterministic role in GABA release at the afferent nerve terminal, the postsynaptic OT neuron effectively filters synaptic signals and thereby modulates its own activity patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".