Effect of localized innervation of the dendritic trees of feline motoneurons on the amplification of synaptic input: a computational study
Bibliographic record
Abstract
Previous studies show that the activation of voltage-dependent channels is dependent on the local density of synapses in the dendritic region containing voltage-dependent channels. We hypothesized that the selective innervation of excitatory vestibulospinal (VST) neurons on the medial dendrites of contralateral splenius motoneurons is designed to enhance the activation of persistent inward currents (PICs) mediated by dendritic L-type Ca(2+) channels. Using compartmental models of splenius motoneurons we compared the synaptic current reaching the soma in response to excitatory input generated by synapses with two different distribution patterns. The medial distribution was based on the arrangement of VST synapses on the dendrites of contralateral splenius motoneurons and the uniform distribution was based on an arrangement of synapses with no particular bias to any region of the dendritic tree. The number of synapses in each distribution was designed to match estimates of the number of VST synapses activated by head movements. In the absence of PICs, the current delivered by the synapses in the uniform distribution was slightly greater. However, the maximal currents were small, < or = 4.1 nA, regardless of the distribution of synapses. In models equipped with L-type Ca(2+) channels, PIC activation was largely determined by the local density of synapses in proximity to the L-type Ca(2+) channels. In 3 of 5 cells, this led to a 2- to 4-fold increase in the current generated by synapses in the medial distribution compared to the uniform distribution. In the other two cells, the amplification bias was in favour of the medial distribution but was either small or restricted to a narrow range of frequencies. These simulations suggest that the innervation pattern of VST axons on contralateral splenius motoneurons is arranged to strengthen an otherwise weak synaptic input by increasing the likelihood of activating PICs. Additional simulations suggest that this prediction can be tested using common experimental protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".