Rhythmically Discharging Basal Forebrain Units Comprise Cholinergic, GABAergic, and Putative Glutamatergic Cells
Bibliographic record
Abstract
The basal forebrain plays important roles in arousal, learning, and memory by stimulating cortical activation characterized by rhythmic slow theta and high-frequency beta-gamma activities. Although cholinergic neurons play a significant part in these roles, other, including GABAergic, neurons appear to contribute. Using juxtacellular labeling with neurobiotin of neurons recorded within the magnocellular preoptic-substantia innominata area in urethan-anesthetized rats, we show that in addition to cells that are cholinergic or GABAergic, other cells that are neither fire rhythmically in correlation with stimulation-induced rhythmic slow activity on the cortex. Neurons with the characteristics of the noncholinergic/nonGABAergic cells contain phosphate-activated glutaminase (PAG), the synthetic enzyme for transmitter glutamate and may thus be glutamatergic. Within their oscillatory spike trains, putative glutamatergic neurons fire at a lower frequency (~20 Hz) than the GABAergic neurons (~40 Hz) and the cholinergic neurons (average: 75 Hz), whose spike trains include high-frequency bursts. The three groups all discharge rhythmically at a slow frequency in correlation with rhythmic slow activity recorded on the prefrontal, entorhinal, piriform and olfactory bulb cortices. The predominant slow frequency corresponds to the respiratory-olfactory rhythm, which is commonly slower than, yet can be as fast as, the hippocampal theta rhythm during certain coordinated behaviors, such as sniffing-whisking. While stimulating higher frequency beta-gamma activities, putative glutamatergic together with GABAergic and cholinergic cells may thus collectively modulate rhythmic slow activity and thereby promote coherent processing and plasticity across distributed cortical networks during coordinated behaviors and states.
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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".