Simulation of Ca<sup>2+</sup> persistent inward currents in spinal motoneurones: mode of activation and integration of synaptic inputs
Bibliographic record
Abstract
The goal of this study was to investigate the nature of activation of the dendritic calcium persistent inward current (Ca(2+) PIC) and its contribution to the enhancement and summation of synaptic inputs in spinal motoneurones. A compartmental cable model of a cat alpha-motoneurone was developed comprising the realistic dendritic distribution of Ia-afferent synapses and low-voltage-activated L-type calcium (Ca(v)1.3) channels distributed over the dendrites in a manner that was previously shown to match a wide set of experimental measurements. The level of synaptic activation was systematically increased and the resulting firing rate, somatic and dendritic membrane potentials, dendritic Ca(v)1.3 channel conductance, and dendritic Ca(2+) PIC were measured. Our simulation results suggest that during cell firing the dendritic Ca(2+) PIC is not activated in an all-or-none manner. Instead, it is initially activated in a graded manner with increasing synaptic input until it reaches its full activation level, after which additional increases in synaptic input result in minimal changes in the Ca(2+) PIC (PIC saturated). The range of graded activation of Ca(2+) PIC occurs when the cell is recruited and causes a steep increase in the firing frequency as the synaptic current is increased, coinciding with the secondary range of the synaptic frequency-current (F-I) relationship. Once the Ca(2+) PIC is saturated the slope of the F-I relationship is reduced, corresponding to the tertiary range of firing. When the post-spike afterhyperpolarization (AHP) is blocked, either directly by blocking the calcium-activated potassium channels, or indirectly by blocking the sodium spikes, the PIC is activated in an all-or-none manner with increasing synaptic input. Thus, the AHP serves to limit the depolarization of the cell during firing and enables graded, rather than all-or-none, activation of the Ca(2+) PIC. The graded activation of the Ca(2+) PIC with increasing synaptic input results in a graded (linear) enhancement and linear summation of synaptic inputs. In contrast, the saturated Ca(2+) PIC enhances synaptic inputs by a constant amount (constant current), and leads to less-than linear summation of multiple synaptic inputs. These model predictions improve our understanding of the mode of activation of the dendritic Ca(2+) PIC and its role in the enhancement and integration of synaptic inputs.
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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 | 0.000 |
| 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".