Synaptic integration in motoneurons with hyper-excitable dendrites
Bibliographic record
Abstract
Motoneurons have extensive dendritic trees that receive the numerous inputs required to produce movement. These dendrites are highly active, containing voltage-sensitive channels that generate persistent inward currents (PICs) that can enhance synaptic input 5-fold or more. However, this enhancement is proportional to the level of activity of monoaminergic inputs from the brainstem that release serotonin and noradrenalin. The higher this activity, the larger the dendritic PIC and the higher the firing rate evoked by a given amount of excitatory synaptic input. This brainstem control of motoneuron input-output gain translates directly into control of system gain of a motor pool and its muscle. Because large dendritic PICs are probably necessary for motoneurons to have sufficient gain to generate large forces, it is possible that descending monoaminergic inputs scale in proportion to voluntary force. Inhibition from sensory inputs has a strong suppressive effect on dendritic PICs: the stronger the inhibition, the smaller the PIC. Thus, local inhibitory inputs within the cord may oppose the descending monoaminergic control of PICs. Most motor behaviors evoke a mixture of excitation and inhibition (e.g., the reciprocal inhibition between antagonists). Therefore, normal joint movements may involve constant adjustment of PIC amplitude.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".