Multiple Modes of Amplification of Synaptic Inhibition to Motoneurons by Persistent Inward Currents
Bibliographic record
Abstract
The ability of inhibitory synaptic inputs to dampen the excitability of motoneurons is augmented when persistent inward currents (PICs) are activated. This amplification could be due to an increase in the driving potential of inhibitory synapses or the deactivation of the channels underlying PICs. Our goal was to determine which mechanism leads to the amplification of inhibitory inputs by PICs. To reach this goal, we measured inhibitory postsynaptic currents (IPSCs) in decerebrate cats during somatic voltage-clamp steps. These IPSCs were generated by tonic activation of Renshaw cells. The IPSCs exhibited a rapid rise and a slower decay to a plateau level. Activation of PICs always led to an increase in the peak of the IPSC, but the amount of decay after the peak of the IPSC was inversely related to the size of the IPSC. These results were replicated in simulations based on compartmental models of motoneurons incorporating distributions of Renshaw cell synapses based on anatomical observations, and L-type calcium channels distributed as 100-microm-long hot spots centered 100 to 400 microm away from the soma. For smaller IPSCs, amplification by PICs was due to an increase in the driving force of the inhibitory synaptic current. For larger IPSCs, amplification was caused by deactivation of the channels underlying PICs leading to a lesser decay of the IPSCs. As a result of this change in the time course of the IPSC, deactivation of the channels underlying PICs leads to a greater amplification of the total inhibitory synaptic current.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".