Pavlovian Control of Intraspinal Microstimulation to Produce Over-Ground Walking
Bibliographic record
Abstract
ABSTRACT Objective Neural interface technologies are more commonly used in people with neural injury. To achieve a symbiotic relationship between device and user, the control system of the device must augment remaining function and adapt to day-to-day changes. The goal of this study was to develop predictive control strategies to produce alternating, over-ground walking in a cat model of hemisection spinal cord injury (SCI) using intraspinal microstimulation (ISMS). Approach Eight cats were anaesthetized and placed in a sling over a walkway. The residual function of a hemisection SCI was mimicked by manually moving one hind-limb through the walking cycle over the walkway. ISMS targeted motor networks in the lumbosacral enlargement to activate muscles in the other limb using low levels of current (< 130 µA). Four different people took turns to move the “intact” limb. Two control strategies, which used ground reaction force and angular velocity information about the manually moved limb to control the timing of the transitions of the other limb, were compared. The first strategy, reaction-based control, used thresholds on the sensor values to initiate state transitions. The other strategy used a reinforcement learning strategy, Pavlovian control, to learn predictions about the sensor values. Thresholds on the predictions were used to initiate transitions. Main Results Both control strategies were able to produce alternating, over-ground walking. Reaction-based control required manual tuning of the thresholds for each person to produce walking, whereas Pavlovian control did not. We demonstrate that learning occurs quickly during walking. Predictions of the sensor signals were learned quickly, initiating transitions in no more than 4 steps. Pavlovian control was resilient to transitions between people walking the limb, between cat experiments, and recovered from mistakes during walking. Significance This work demonstrates that Pavlovian control can augment remaining function and allow for personalized walking with minimal tuning requirements.
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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) | 0.001 | 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".