Decoding sensory feedback from firing rates of afferent ensembles recorded in cat dorsal root ganglia in normal locomotion
Bibliographic record
Abstract
Sensory feedback is required by biological motor control systems to maintain stability, respond to perturbations, and adapt. Similarly, motor neuroprostheses require feedback to provide natural and complete restoration of motor functions. In this paper, we show that ensemble firing rates from the body's mechanoreceptors can provide a natural source of kinematic state feedback and could be useful for prosthetic control. Single unit recordings from multiple primary afferent neurons were obtained during walking using multichannel electrode arrays implanted chronically in the L7 dorsal root ganglia of three cats. We typically recorded simultaneously from over 20-30 neurons during the first 7-14 days after surgery, but recordings gradually worsened thereafter. Histology indicates that a ring of inflammatory and connective tissues (100 microm thick) develops around each microelectrode and likely contributes to the degradation in recording quality. Accurate estimates of the hindlimb trajectory were made using a linear filter with inputs from only a few neurons highly correlated with limb kinematics. The coefficients for the linear filter were identified in a least-squares fit with 5-10 s of walking data (model training stage). The estimated and actual trajectories of separate walking data generally match well for walking at a range of speeds accounting for 63 +/- 22% (mean +/- S.D. for hip, knee, and ankle) of the variance in joint angle and 72 +/- 4% of the variance in joint angular velocities. These results indicate that a neural interface with primary sensory neurons in the dorsal root ganglion can provide accurate kinematic state information that may be useful for closed loop control of a neuroprosthesis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".