Reliability of triggering postinhibitory rebound bursts in deep cerebellar neurons
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Bibliographic record
Abstract
Deep cerebellar nuclear (DCN) neurons exhibit distinct phenotypes of rebound discharge following current-evoked membrane hyperpolarizations that arise from specific Ca(V)3 T-type Ca(2+) channel isoforms and Ca(2+)-activated K(+) channels. The probability of evoking rebound bursts following a brief train of GABAergic inhibitory synaptic input from cerebellar Purkinje cells was recently questioned for stimulus intensities adjusted to evoke a long post-stimulus pause in spike firing. We revisited this issue to examine the potential for generating rebound bursts in DCN cells in response to synaptic inputs in vitro. Both a Transient and Weak Burst phenotype could be distinguished in on-cell extracellular recordings or whole-cell recordings in response to inhibitory synaptic input. We found that the rebound burst response was a sensitive function of stimulus intensity, such that increasing stimulus intensity significantly raised the probability for evoking bursts while decreasing pause duration. The threshold for reliably generating rebound bursts was approximately 40 percent of maximum intensity, a level that evokes an IPSC corresponding to only a small number of the inhibitory terminals known to impinge on DCN cells. The probability for evoking rebound bursts is thus very high for moderate levels of stimulation in vitro, leaving the potential for rebound discharge to contribute to signal processing in vivo an open question.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it