High-Threshold K <sup>+</sup> Current Increases Gain by Offsetting a Frequency-Dependent Increase in Low-Threshold K <sup>+</sup> Current
Bibliographic record
Abstract
High-frequency firing neurons are found in numerous central systems, including the auditory brainstem, thalamus, hippocampus, and neocortex. The kinetics of high-threshold K+ currents (IK(HT)) from the Kv3 subfamily has led to the proposal that these channels offset cumulative Na+ current inactivation and stabilize tonic high-frequency firing. However, all high-frequency firing neurons, examined to date, also express low-threshold K+ currents (IK(LT)) that have slower kinetics and play an important role in setting the subthreshold and filtering properties of the neuron. IK(LT) has also been shown to dampen excitability and is therefore likely to oppose high-frequency firing. In this study, we examined the role of IK(HT) in pyramidal cells of the electrosensory lobe of weakly electric fish, which are characterized by high-frequency firing, a very wide frequency range, and high levels of IK(HT). In particular, we examined the mechanisms that allow IK(HT) to set the gain of the F-I relationship by interacting with another low-threshold K+ current. We found that IK(HT) increases the gain of the F-I relationship and influences spike waveform almost exclusively in the high-frequency firing range. The frequency dependence arises from IK(HT) influencing both the IK(LT) and Na+ currents. IK(HT) thus plays a significant role in stabilizing high-frequency firing by preventing a steady-state accumulation of IK(LT) that is as important as preventing Na+ current inactivation.
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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) | 0.002 | 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".