Sensorimotor control of swimming <i>Polypterus senegalus</i> is preserved during sensory deprivation conditions across altered environments
Bibliographic record
Abstract
Control of locomotion involves the interplay of sensory signals and motor commands. Sensory information is essential for adjusting locomotion in response to environmental changes. A previous study using mathematical modelling of lamprey swimming has shown that, in the absence of sensory feedback, increasing fluid viscosity constrains swimming kinematics, limiting tail amplitude and body wavelength, resulting in decreased swimming speed. In contrast, previous experiments with Polypterus senegalus reported increased magnitude swimming kinematics (increased body curvature, body wave speed and frequency, and pectoral fin frequency) in high viscosity water suggesting that sensory information is used to adjust swimming form. It is not known what sensory systems are providing the necessary information to respond to these environmental changes. We tested the hypothesis that lateral line and visual input are responsible for the sensory-driven increase in swimming kinematics in response to experimentally increased fluid viscosity. The kinematics of five P. senegalus were recorded in two different viscosities of water while removing lateral line and visual sensory feedback. Unlike the mathematical model devoid of sensory feedback, P. senegalus with lateral line and/or visual senses removed did not reduce the magnitude of swimming kinematic variables, suggesting that additional sensory feedback mechanisms are present in these fish to help overcome increased fluid viscosity. Increases in swimming speed when both lateral line and visual sensory feedback were removed suggest that lateral line and visual information may be used to regulate swimming speed in P. senegalus, possibly using an internal model of predictions to adjust swimming form.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".