15.4 A Neuroprosthetic SoC with Sensory Feedback Featuring Frequency-Splitting-Based Wireless Power Transfer with 200Mb/s 0.67pJ/b Backscatter Data Uplink and Unsupervised Multi-Class Spike Sorting
Bibliographic record
Abstract
Neuroprosthetic technology has made significant strides in restoring movement for paralyzed individuals by decoding motor cortex signals into commands for prosthetic limbs or exoskeletons. Although high-channel neural implants have enhanced prosthetic control and freedom of movement, the benefits of channel scaling are restricted by the absence of feedback and the steep learning curves required for users. To address this, sensory feedback from prosthetic sensors has been used to modulate the sensory cortex for more stable and accurate prosthetic control [1]. However, latency in data transmission, decoding, and feedback mapping hinders the system's effectiveness as a true closed-loop. To overcome these challenges, a disruptive approach has emerged that introduces rapid local feedback between the motor and sensory cortices [2]. Instead of relying on external sensor inputs, this method derives sensory feedback directly from motor signals and modulates stimulation in the sensory cortex, reducing latency and simplifying learning (Fig. 15.4.1 top). Animal studies using optogenetic feedback have shown faster motor target acquisition with this internal feedback [3]. While optogenetics cannot be easily adopted for humans, electrical stimulation is a viable alternative, though it presents challenges such as rejecting stimulation artifacts to avoid false modulation or positive feedback loops.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".