Integration Time in a Subset of Spinal Lamina I Neurons Is Lengthened by Sodium and Calcium Currents Acting Synergistically to Prolong Subthreshold Depolarization
Bibliographic record
Abstract
Lamina I of the spinal dorsal horn plays an important role in processing and relaying nociceptive information to the brain. It comprises physiologically distinct cell types that process information in fundamentally different ways: tonic neurons fire repetitively during stimulation and display prolonged EPSPs, suggesting operation as integrators, whereas single-spike neurons act like coincidence detectors. Using whole-cell recordings from a rat spinal slice preparation, we set out to determine the basis for prolonged EPSPs in tonic cells and the implications for signal processing. Kinetics of synaptic currents could not explain differences in EPSP kinetics. Instead, tonic neurons were found to express a persistent sodium current, I(Na,P), that amplified and prolonged depolarization in response to brief stimulation. Tonic neurons also expressed a persistent calcium current, I(Ca,P), that contributed to prolongation but not to amplification. Simulations using NEURON software demonstrated that I(Na,P) was necessary and sufficient to explain amplification, whereas I(Na,P) and I(Ca,P) acted synergistically to prolong depolarization: initial activation of the slower current (I(Ca,P)) depended on the faster current (I(Na,P)) but maintained activation of the faster current likewise depended on the slower current. Additional investigation revealed that I(Na,P) and I(Ca,P) could dramatically increase integration time (>30x) and thereby encourage temporal summation but at the expense of spike time precision. Thus, by prolonging subthreshold depolarization, intrinsic inward currents allow tonic neurons in spinal lamina I to specialize as integrators that are optimally suited to encode stimulus intensity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".