Locus Ceruleus Activation Suppresses Feedforward Interneurons and Reduces β-γ Electroencephalogram Frequencies While It Enhances θ Frequencies in Rat Dentate Gyrus
Bibliographic record
Abstract
The locus ceruleus is activated by novel stimuli, and its activation promotes learning and memory. Phasic activation of locus ceruleus neurons by glutamate enhances the dentate gyrus population spike amplitude and results in long-term potentiation of synaptic responses recorded after 24 h. Cholinergic activation of locus ceruleus neurons increases hippocampal . At the level of the cellular network, it is not clear how the potentiating effects of norepinephrine are mediated. Previous studies show that exogenous norepinephrine enhances inhibitory interneuron firing in the dentate gyrus. This finding appears at odds with evidence for potentiation. In this study, natural release of norepinephrine was induced by glutamate activation of locus ceruleus while we recorded EEGs and physiologically identified interneurons in the dentate gyrus of urethane-anesthetized rats. Feedforward neurons were inhibited (approximately 1-2 min) by locus ceruleus activation. Feedback interneurons showed both increased and decreased activity, whereas granule cells increased firing as predicted by evoked potential studies. EEG results replicated an increase in power (4-8 Hz) with locus ceruleus activation, but the effect with glutamatergic locus ceruleus activation was transient (approximately 1-2 min). Beta-gamma Frequencies were also transiently suppressed. Together, the data suggest that locus ceruleus activation enhances the throughput of concomitant sensory input by reducing feedforward inhibitory interneuron activity, which may reduce "binding" in existing cell assemblies, and enhances the conditions for synaptic plasticity through disinhibition, promotion of 4-8 Hz , and noradrenergic potentiation to facilitate the building of new representations.
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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".