Inhibitory Coupling between Inhibitory Interneurons in the Spinal Cord Dorsal Horn
Bibliographic record
Abstract
Local inhibitory interneurons in the dorsal horn play an important role in the control of excitability at the segmental level and thus determine how nociceptive information is relayed to higher structures. Regulation of inhibitory interneuron activity may therefore have critical consequences on pain perception. Indeed, disinhibition of dorsal horn neuronal networks disrupts the balance between excitation and inhibition and is believed to be a key mechanism underlying different forms of pain hypersensitivity and chronic pain states. In this context, studying the source and the synaptic properties of the inhibitory inputs that the inhibitory interneurons receive is important in order to predict the impact of drug action at the network level. To address this, we studied inhibitory synaptic transmission in lamina II inhibitory interneurons identified under visual guidance in spinal slices taken from transgenic mice expressing enhanced green fluorescent protein (EGFP) under the control of the GAD promoter. The majority of these cells fired tonically to a long depolarizing current pulse. Monosynaptically evoked inhibitory postsynaptic currents (eIPSCs) in these cells were mediated by both GABAA and glycine receptors. Consistent with this, both GABAA and glycine receptor-mediated miniature IPSCs were recorded in all of the cells. These inhibitory inputs originated at least in part from local lamina II interneurons as verified by simultaneous recordings from pairs of EGFP+ cells. These synapses appeared to have low release probability and displayed potentiation and asynchronous release upon repeated activation. In summary, we report on a previously unexamined component of the dorsal horn circuitry that likely constitutes an essential element of the fine tuning of nociception.
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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 | 0.000 |
| 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 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".