Contributions of Voltage- and Ca<sup>2+</sup>-Activated Conductances to GABA-Induced Depolarization in Spider Mechanosensory Neurons
Bibliographic record
Abstract
Activation of ionotropic gamma-aminobutyric acid type A (GABA(A)) receptors depolarizes neurons that have high intracellular [Cl(-)], causing inhibition or excitation in different cell types. The depolarization often leads to inactivation of voltage-gated Na channels, but additional ionic mechanisms may also be affected. Previously, a simulated model of spider VS-3 mechanosensory neurons suggested that although voltage-activated Na(+) current is partially inactivated during GABA-induced depolarization, a slowly activating and inactivating component remains and may contribute to the depolarization. Here, we confirmed experimentally, by blocking Na channels prior to GABA application, that Na(+) current contributes to GABA-induced depolarization in VS-3 neurons. Ratiometric Ca(2+) imaging experiments combined with intracellular recordings revealed a significant increase in intracellular [Ca(2+)] when GABA(A) receptors were activated, synchronous with the depolarization and probably due to Ca(2+) influx via low-voltage-activated (LVA) Ca channels. In contrast, GABA(B)-receptor activation in these neurons was previously shown to inhibit LVA current. Blockade of voltage-gated K channels delayed membrane repolarization, extending GABA-induced depolarization. However, inhibition of Ca channels significantly increased the amplitude of GABA-induced depolarization, indicating that Ca(2+)-activated K(+) current has an even stronger repolarizing effect. Regulation of intracellular [Ca(2+)] is important for many cellular processes and Ca(2+) control of K(+) currents may be particularly important for some functions of mechanosensory neurons, such as frequency tuning. These data show that GABA(A)-receptor activation participates in this regulation.
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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 | 0.001 |
| 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".