Role of High-Voltage Activated Potassium Currents in High-Frequency Neuronal Firing: Evidence From a Basal Metazoan
Bibliographic record
Abstract
Certain neurons of vertebrates are specialized for high-frequency firing. Interestingly, high-frequency firing is also seen in central neurons in basal bilateral metazoans. Recently, the role of potassium currents with rightward-shifted activation curves in producing high-frequency firing has come under scrutiny. We apply intracellular recording, patch-clamp techniques, and compartmental modeling to examine the roles of rightward-shifted potassium currents in repetitive firing and shaping of action potentials in central neurons of the flatworm, Notoplana atomata (Phylum Platyhelminthes). The kinetic properties of potassium and sodium currents were determined from patch-clamp experiments on dissociated brain cells. To predict the effects of changing the steady-state and kinetic properties of these potassium currents, these data were incorporated into a computer model of a 30-microm spherical cell with the levels of current adjusted to approximate the values recorded in voltage-clamp experiments. The model was able to support regenerative spikes at high frequencies in response to injected current. Current-clamp recordings of cultured cells and of neurons in situ also showed evidence of very-high-frequency firing. Adjusting the ratio of inactivating to non-inactivating potassium currents had little effect upon the firing pattern of the cell or its ability to fire at high frequencies, whereas the presence of the non-inactivating current was necessary for repetitive firing. Computer simulations suggested that the rightward shift in voltage sensitivity confers a raised firing threshold, while rapid channel kinetics underlie high frequency firing, and the large activation range enhances the coding range of the cell.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".