Kv3 K<sup>+</sup> channels enable burst output in rat cerebellar Purkinje cells
Bibliographic record
Abstract
The ability of cells to generate an appropriate spike output depends on a balance between membrane depolarizations and the repolarizing actions of K(+) currents. The high-voltage-activated Kv3 class of K(+) channels repolarizes Na(+) spikes to maintain high frequencies of discharge. However, little is known of the ability for these K(+) channels to shape Ca(2+) spike discharge or their ability to regulate Ca(2+) spike-dependent burst output. Here we identify the role of Kv3 K(+) channels in the regulation of Na(+) and Ca(2+) spike discharge, as well as burst output, using somatic and dendritic recordings in rat cerebellar Purkinje cells. Kv3 currents pharmacologically isolated in outside-out somatic membrane patches accounted for approximately 40% of the total K(+) current, were very fast and high voltage activating, and required more than 1 s to fully inactivate. Kv3 currents were differentiated from other tetraethylammonium-sensitive currents to establish their role in Purkinje cells under physiological conditions with current-clamp recordings. Dual somatic-dendritic recordings indicated that Kv3 channels repolarize Na(+) and Ca(2+) spikes, enabling high-frequency discharge for both types of cell output. We further show that during burst output Kv3 channels act together with large-conductance Ca(2+)-activated K(+) channels to ensure an effective coupling between Ca(2+) and Na(+) spike discharge by preventing Na(+) spike inactivation. By contributing significantly to the repolarization of Na(+) and especially Ca(2+) spikes, our data reveal a novel function for Kv3 K(+) channels in the maintenance of high-frequency burst output for cerebellar Purkinje cells.
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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".