Independent Control of Presynaptic Inhibition by Reticulospinal and Sensory Inputs at Rest and during Rhythmic Activities in the Cat
Bibliographic record
Abstract
To be functionally relevant during movement, the transmission from primary afferents must be efficiently controlled by presynaptic inhibition. Sensory feedback, central pattern generators, and supraspinal structures can all evoke presynaptic inhibition, but we do not understand how these inputs interact during movement. Here, we investigated the convergence of inputs from the reticular formation and sensory afferents on presynaptic inhibitory pathways and their modulation at rest and during two fictive motor tasks (locomotion and scratch) in decerebrate cats. The amplitude of primary afferent depolarization (PAD), an estimate of presynaptic inhibition, was recorded in individual afferents with intra-axonal recordings and in a mix of afferents in lumbar dorsal rootlets (dorsal root potential [DRP]) with bipolar electrodes. There was no spatial facilitation between inputs from reticulospinal and sensory afferents with DRPs or PADs, indicating an absence of convergence. However, spatial facilitation could be observed by combining two sensory inputs, indicating that convergence was possible. Task-dependent changes in the amplitude of responses were similar for reticulospinal and sensory inputs, increasing during fictive locomotion and decreasing during fictive scratch. During fictive locomotion, DRP and PAD amplitudes evoked by reticulospinal inputs were increased during the flexion phase, whereas sensory-evoked DRPs and PADs showed maximal amplitude in either flexion or extension phases. During fictive scratch, the amplitudes of DRPs and PADs evoked by both sources were maximal in flexion. The absence of spatial facilitation and different phase-dependent modulation patterns during fictive locomotion are consistent with independent presynaptic inhibitory pathways for reticulospinal and sensory 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 | 0.000 |
| 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) | 0.000 | 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".