A Cellular Mechanism for the Transformation of a Sensory Input into a Motor Command
Bibliographic record
Abstract
The initiation and control of locomotion largely depend on processing of sensory inputs. The cellular bases of locomotion have been extensively studied in lampreys where reticulospinal (RS) neurons constitute the main descending system activating and controlling the spinal locomotor networks. Ca(2+) imaging and intracellular recordings were used to study the pattern of activation of RS neurons in response to cutaneous stimulation. Pressure applied to the skin evoked a linear input/output relationship in RS neurons until a threshold level, at which a depolarizing plateau was induced, the occurrence of which was associated with the onset of swimming activity in a semi-intact preparation. The occurrence of a depolarizing plateau was abolished by blocking the NMDA receptors that are located on RS cells. Moreover, the depolarizing plateaus were accompanied by a rise in [Ca(2+)](i), and an intracellular injection of the Ca(2+) chelator BAPTA into single RS cells abolished the plateaus, suggesting that the latter are Ca(2+) dependent and rely on intrinsic properties of RS cells. The plateaus were shown to result from the activation of a Ca(2+)-activated nonselective cation current that maintains the cell in a depolarized state. It is concluded that this intrinsic property of the RS neuron is then responsible for the transformation of an incoming sensory signal into a motor command that is then forwarded to the spinal locomotor networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".