Model Vestibular Nuclei Neurons Can Exhibit a Boosting Nonlinearity Due to an Adaptation Current Regulated by Spike-Triggered Calcium and Calcium-Activated Potassium Channels
Bibliographic record
Abstract
In vitro studies have previously found a class of vestibular nuclei neurons to exhibit a bidirectional afterhyperpolarization (AHP) in their membrane potential, due to calcium and calcium-activated potassium conductances. More recently in vivo studies of such vestibular neurons were found to exhibit a boosting nonlinearity in their input-output tuning curves. In this paper, a Hodgkin-Huxley (HH) type neuron model, originally developed to reproduce the in vitro AHP, is shown to produce a boosting nonlinearity similar to that seen in vivo for increased the calcium conductance. Indicative of a bifurcation, the HH model is reduced to a generalized integrate-and-fire (IF) model that preserves the bifurcation structure and boosting nonliearity. By then projecting the neuron model's phase space trajectories into 2D, the underlying geometric mechanism relating the AHP and boosting nonlinearity is revealed. Further simplifications and approximations are made to derive analytic expressions for the steady steady state firing rate as a function of bias current, μ, as well as the gain (i.e. its slope) and the position of its peak at μ = μ*. Finally, although the boosting nonlinearity has not yet been experimentally observed in vitro, testable predictions indicate how it might be found.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".