Spike frequency adaptation of rat hindlimb motoneurons
Bibliographic record
Abstract
The objective of our study was to resolve two issues pertaining to motoneuron (MN) spike frequency adaptation (SFA): 1) to develop an index of SFA that is sensitive to a wide range of adaptation patterns and would correlate well with MN excitability and 2) to determine whether SFA pattern is stimulus current dependent. Sprague-Dawley rats (250-350 g) were anesthetized (ketamine-xylazine) before electrophysiological properties from sciatic nerve MNs located in the lumbar spinal cord were recorded. SFA was measured by 30-s square-wave current injections at 1.5, 3.0, and 5.0 nA above estimated rhythmic firing threshold. Discharges per second were significantly (P < 0.001) higher for 5-nA than for 1.5- and 3-nA currents > rhythmic firing threshold in the first 2 s. SFA was quantified by using ratios of the final to initial number of discharges with 1-, 2-, and 5-s bins. The best index of SFA was the percent decline in the number of spikes fired in the fifth 5-s bin relative to the first 5-s bin [1 - (bin 5/bin 1)]. With the use of this index, we found that SFA was significantly correlated with several measures of MN excitability, including estimated persistent inward current amplitude (r = -0.76) and rheobase current (r = 0.71), and tended to correlate with input resistance (r = -0.43) and frequency-current slope (r = -0.57). This index also showed the widest range of SFA among MNs. In conclusion, an SFA pattern can be ascertained for each MN and becomes more pronounced as MN excitability decreases. Finally, for the first time, we report evidence of a relationship between persistent inward current and SFA.
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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 | 0.000 | 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 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".