Discharge Profiles across the Sleep–Waking Cycle of Identified Cholinergic, GABAergic, and Glutamatergic Neurons in the Pontomesencephalic Tegmentum of the Rat
Bibliographic record
Abstract
Distributed within the laterodorsal tegmental and pedunculopontine tegmental nuclei (LDT and PPT), cholinergic neurons in the pontomesencephalic tegmentum have long been thought to play a critical role in stimulating cortical activation during waking (W) and paradoxical sleep (PS, also called REM sleep), yet also in promoting PS with muscle atonia. However, the discharge profile and thus precise roles of the cholinergic neurons have remained uncertain because they lie intermingled with GABAergic and glutamatergic neurons, which might also assume these roles. By applying juxtacellular recording and labeling in naturally sleeping-waking, head-fixed rats, we investigated the discharge profiles of histochemically identified cholinergic, GABAergic, and glutamatergic neurons in the LDT, SubLDT, and adjoining medial part of the PPT (MPPT) in relation to sleep-wake states, cortical activity, and muscle tone. We found that all cholinergic neurons were maximally active during W and PS in positive correlation with fast (γ) cortical activity, as "W/PS-max active neurons." Like cholinergic neurons, many GABAergic and glutamatergic neurons were also "W/PS-max active." Other GABAergic and glutamatergic neurons were "PS-max active," being minimally active during W and maximally active during PS in negative correlation with muscle tone. Conversely, some glutamatergic neurons were "W-max active," being maximally active during W and minimally active during PS in positive correlation with muscle tone. Through different discharge profiles, the cholinergic, GABAergic, and glutamatergic neurons of the LDT, SubLDT, and MPPT thus appear to play distinct roles in promoting W and PS with cortical activation, PS with muscle atonia, or W with muscle tone.
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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) | 0.001 | 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".