EMG spike time difference based feedback control
Bibliographic record
Abstract
Flight control in insects has been studied extensively; however the underlying neural mechanisms are not fully understood. Output from the central nervous system (CNS) must drive wing phase shifts and flight muscle depressor asymmetries associated with adaptive flight maneuvers. These maneuvers will, in turn, influence the insect's sensory environment, thus closing the feedback loop. We present a novel method that utilizes asymmetrical timing of bilateral depressor muscles, the forewing first basalars (m97), of the locust to close a visual feedback loop in a computer-generated flight simulator. The method converts the time difference between left and right m97s to analog voltage values. These voltage values can be obtained using open-loop experiments (visual motion controlled by the experimenter), or can be used to control closed-loop experiments (muscle activity controls the visual stimuli) experiments. Electromyographic (EMG) signals were obtained from right and left m97 muscles; spike time difference between them was calculated and converted to voltage values. Testing this circuit with real animals, we were able to detect the spike time difference and convert that to voltage that controlled the presentation of a stimulus in a closed-loop environment. This method may be used in conjunction with the flight simulator to understand the manner in which sensory information is integrated with the activity of the flight circuitry to study the neural control of this complex behaviour.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".