Intermediate conductance calcium-activated potassium channels modulate summation of parallel fiber input in cerebellar Purkinje cells
Bibliographic record
Abstract
Encoding sensory input requires the expression of postsynaptic ion channels to transform key features of afferent input to an appropriate pattern of spike output. Although Ca(2+)-activated K(+) channels are known to control spike frequency in central neurons, Ca(2+)-activated K(+) channels of intermediate conductance (KCa3.1) are believed to be restricted to peripheral neurons. We now report that cerebellar Purkinje cells express KCa3.1 channels, as evidenced through single-cell RT-PCR, immunocytochemistry, pharmacology, and single-channel recordings. Furthermore, KCa3.1 channels coimmunoprecipitate and interact with low voltage-activated Cav3.2 Ca(2+) channels at the nanodomain level to support a previously undescribed transient voltage- and Ca(2+)-dependent current. As a result, subthreshold parallel fiber excitatory postsynaptic potentials (EPSPs) activate Cav3 Ca(2+) influx to trigger a KCa3.1-mediated regulation of the EPSP and subsequent after-hyperpolarization. The Cav3-KCa3.1 complex provides powerful control over temporal summation of EPSPs, effectively suppressing low frequencies of parallel fiber input. KCa3.1 channels thus contribute to a high-pass filter that allows Purkinje cells to respond preferentially to high-frequency parallel fiber bursts characteristic of sensory input.
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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".