Re-Engaging Spinal Reflexes: Toward Multisensory Feedback Integration in Neuroprosthetic Control
Bibliographic record
Abstract
Neuroprosthetic devices have evolved significantly over the last two decades. Modern Electromyography (EMG) prostheses and neural EMG prostheses can fully decode the user's intention for basic motor tasks. Bidirectional research has revealed exciting advances, with intraneural stimulation and bionic encoding of tactile and proprioceptive inputs. Thus, even without visual feedback, they can modulate force and other properties of the object they interact with. Clinical trials of neuromusculoskeletal prostheses have shown feasibility for long-term use. This paper applies these ideas: feedback should not be limited to processing artificial sensory signals at the controller level; it should be returned to the spinal reflex pathway to engage intrinsic circuits in low-latency correction, perturbation, and postural stabilization. These frameworks can be illustrated by case studies of intraneural tactile feedback and tendon vibration to understand how users of the prosthesis can restore stability, efficiency, and implement reflex arcs with artificial receptors. Taken together, these findings suggest that the prosthesis can be viewed as an embodied extension of the nervous system rather than a robotic device separated from the body. If neuroprostheses include multimodal sensory feedback and revert to spinal cord level reflexes, these implantable devices have the potential to produce natural motor coordination that remains clinically feasible for daily functioning.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".