The Expression Pattern of a Cav3-Kv4 Complex Differentially Regulates Spike Output in Cerebellar Granule Cells
Bibliographic record
Abstract
The cerebellum receives sensory information by mossy fiber input from a multitude of sources that require differential signal processing. A compartmentalization of function begins with the segregation of mossy fibers across 10 distinct lobules over the rostrocaudal axis, with tactile receptor afferents prevalent in anterior lobules and vestibular input in caudal lobules. However, it is unclear how these unique signals might be differentially processed at the circuit level across the cerebellum. As granule cells receive mossy fiber input, they represent a key stage at which postsynaptic mechanisms could influence signal processing. Granule cells express an A-type current mediated by Kv4 potassium channels that modify the latency and frequency of spike output. The current study examined the potential for a Cav3 calcium-Kv4 channel complex to regulate the response of granule cells to mossy fiber input in lobules 2 and 9 of the rat cerebellum. Similar A-type currents were recorded in both regions, but the Cav3 calcium current was expressed at a substantially higher density in lobule 9 cells, acting to increase A-type current availability through its influence on Kv4 voltage for inactivation. The difference in excitability imparted by Cav3-Kv4 interactions proves to allow lobule 2 granule cells to respond more effectively to tactile stimulus-like burst input and lobule 9 cells to slow shifts in input frequency characteristic of vestibular input. The expression pattern of Cav3 channels and its control of Kv4 availability thus provides a novel means of processing widely different forms of sensory input across cerebellar lobules.
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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".