Synaptic activation of GABA<sub>B</sub> receptors regulates neuronal network activity and entrainment
Bibliographic record
Abstract
In the mammalian central nervous system, GABA(B) receptors mediate slow pre- and postsynaptic inhibition. Using rat hippocampal slices we investigated the role of synaptic GABA(B) receptors in regulating kainate-induced subthreshold neuronal network oscillations in the gamma frequency range (25-80 Hz). The GABA(B) receptor agonist baclofen largely eliminated gamma oscillations. The GABA(B) receptor antagonist CGP55845 reversed this action of baclofen but alone did not alter the power or frequency of ongoing oscillations. To examine the role of synaptically released GABA on network activity, we electrically stimulated stratum radiatum of CA3 whilst recording gamma oscillations from stratum pyramidale. Single stimuli produced a pronounced transient (up to 1 s in duration) inhibition of gamma frequency oscillations. This stimulus-induced shutdown of network activity was enhanced by the GABA uptake inhibitor tiagabine and largely inhibited by CGP55845. Multiple stimuli delivered at frequencies of 1-3 Hz resulted in an activity-dependent fatigue of the inhibition of gamma activity, such that, after a number of stimuli, oscillations could be detected tens of milliseconds after the stimulus. Interestingly, this activity-dependent fatigue of inhibition uncovered a stimulus-dependent temporal entrainment of the gamma oscillations. Furthermore, the amount of repetitive synaptic input that was required to cause this entrainment was dramatically reduced by GABA(B) receptor antagonism such that it was evident within just a few stimuli. These data suggest that convergent afferent synaptic activity can alter the precise temporal arrangement of neuronal network activity. Furthermore, the flow of such information into a functioning neuronal network is highly regulated by GABA(B) receptor-mediated synaptic inhibition.
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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".