Biophysical properties of the membrane influence spike initiation dynamics and neuronal excitability: a focus on Kv1 channels in myelinated axons
Bibliographic record
Abstract
Neurons and their subcellular compartments exhibit distinct forms of excitability. In 1948, Alan Hodgkin described different classes of neuronal excitability, each characterized by unique spiking responses to a constant stimulus. Despite these early insights, the mechanisms by which membrane properties influence spike initiation and excitability remain poorly understood. This review explores the nonlinear dynamics underlying spike initiation across excitability classes, emphasizing how these differences contribute to the neural encoding and processing of diverse information. Within a single neuron, compartments such as the soma, axon initial segment (AIS), and axon can exhibit functionally distinct excitability profiles due to differences in ion channel expression and membrane properties. For instance, the biophysical properties of myelinated axons, particularly the expression and distribution of voltage-gated potassium (Kv1) channels, play a key role in maintaining the directional fidelity of action potential propagation by facilitating orthodromic transmission and suppressing antidromic activity. These compartment-specific dynamics underscore the intricate design of neural systems to maintain the precision and efficiency of neural signalling. Moreover, perturbations in excitability are implicated in various neurological disorders, including epilepsy and chronic pain, highlighting the importance of maintaining physiological excitability profiles. By exploring these mechanisms, this review aims to provide insight into how alterations in membrane biophysics may inform future therapeutic strategies targeting excitability-related pathologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".