SK Channels Provide a Novel Mechanism for the Control of Frequency Tuning in Electrosensory Neurons
Bibliographic record
Abstract
One important characteristic of sensory input is frequency, with sensory neurons often tuned to narrow stimulus frequency ranges. Although vital for many neural computations, the cellular basis of such frequency tuning remains mostly unknown. In the electrosensory system of Apteronotus leptorhynchus, the primary processing of important environmental and communication signals occurs in pyramidal neurons of the electrosensory lateral line lobe. Spike trains transmitted by these cells can encode low-frequency prey stimuli with bursts of spikes and high-frequency communication signals with single spikes. Here, we demonstrate that the selective expression of SK2 channels in a subset of pyramidal neurons reduces their response to low-frequency stimuli by opposing their burst responses. Apamin block of the SK2 current in this subset of cells induced bursting and increased their response to low-frequency inputs. SK channel expression thus provides an intrinsic mechanism that predisposes a neuron to respond to higher frequencies and thus specific, behaviorally relevant stimuli.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".