Intelligent Control of a Spinal Prosthesis to Restore Walking After Neural Injury: Recent Work and Future Possibilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review focuses on the development of intelligent, intuitive control strategies for restoring walking using an innovative spinal neural prosthesis called intraspinal microstimulation (ISMS). These control strategies are inspired by the control of walking by the nervous system and are aimed at mimicking the natural functionality of locomotor-related sensorimotor systems. The work to date demonstrates how biologically inspired control strategies, some including machine learning methods, can be used to augment remaining function in models of complete and partial paralysis developed in anesthetized cats. This review highlights the advantages of learning predictions to produce automatically adaptive control of over-ground walking. This review also speculates on the possible future applications of similar machine learning algorithms for challenging walking tasks including navigating obstacles and traversing difficult terrain. Finally, this review explores the potential for plasticity and motor recovery with long-term use of such intelligent control systems and neural interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it