Contribution of AMPA, NMDA, and GABA<sub>A</sub>Receptors to Temporal Pattern of Postsynaptic Responses in the Inferior Colliculus of the Rat
Bibliographic record
Abstract
The central nucleus of the inferior colliculus (ICC) is a major site of synaptic interaction in the central auditory system. To understand how ICC neurons integrate excitatory and inhibitory inputs for processing temporal information, we examined postsynaptic responses of ICC neurons to repetitive stimulation of the lateral lemniscus at 10-100 Hz in rat brain slices. The excitatory synaptic currents mediated by AMPA and NMDA receptors and the inhibitory current mediated by GABA(A) receptors were pharmacologically isolated and recorded by whole-cell patch-clamp techniques. The response kinetics of AMPA receptor-mediated EPSCs and GABA(A) receptor-mediated IPSCs were similar and much faster than those of NMDA receptor-mediated EPSCs. AMPA EPSCs could follow each pulse of stimulation at a rate of 10-100 Hz but showed response depression during the course of repetitive stimulation. GABA(A) IPSCs could also follow stimulus pulses over this frequency range but showed depression at low rates and facilitation at higher rates. NMDA EPSCs showed facilitation and temporal summation in response to repetitive stimulation, which was most pronounced at higher rates of stimulation. GABA(A) inhibition suppressed activation of NMDA receptors and reduced both the degree of AMPA EPSC depression and the extent of temporal summation of NMDA EPSCs. The results indicate that GABA(A) receptor-mediated inhibition plays a crucial role in maintaining the balance of excitation and inhibition and in allowing ICC neurons to process temporal information more precisely.
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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".