Network and Intrinsic Contributions to Carbachol-Induced Oscillations in the Rat Subiculum
Bibliographic record
Abstract
Low-frequency network oscillations occur in several areas of the limbic system where they contribute to synaptic plasticity and mnemonic functions that are in turn modulated by cholinergic mechanisms. Here we used slices of the rat subiculum (a limbic area involved in cognitive functions) to establish how network and single neuron (intrinsic) membrane mechanisms participate to the rhythmic oscillations elicited by the cholinergic agent carbachol (CCh, 50-100 microM). We have found that CCh-induced network oscillations (intraoscillatory frequency = 5-16 Hz) are abolished by an antagonist of non-N-methyl-D-aspartate (NMDA) glutamatergic receptors (n = 6 slices) but persist during blockade of GABA receptors (n = 16). In addition, during application of glutamate and GABA receptor antagonists, single subicular cells generate burst oscillations at 2.1-6.8 Hz when depolarized with steady current injection. These intrinsic burst oscillations disappear during application of a Ca(2+) channel blocker (n = 6 cells), intracellular Ca(2+) chelation (n = 6), or replacement of extracellular Na(+) (n = 4) but persist in recordings made with electrodes containing a blocker of voltage-gated Na(+) channels (n = 7). These procedures cause similar effects on CCh-induced depolarizing plateau potentials that are contributed by a Ca(2+)-activated nonselective cationic conductance (I(CAN)). Network and intrinsic oscillations along with depolarizing plateau potentials were abolished by the muscarinic receptor antagonist atropine. In conclusion, our findings demonstrate that low-frequency oscillations in the rat subiculum rely on the muscarinic receptor-dependent activation of an intrinsic oscillatory mechanism that is presumably contributed by I(CAN) and are integrated within the network via non-NMDA receptor-mediated transmission.
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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".