Multiplierless Spiking Neural Network for Motor Signal Decoding in the Peripheral Nervous System
Bibliographic record
Abstract
The peripheral nervous system (PNS) facilitates communication between the brain and various organs. Advanced PNS neural interfaces can help in restoring motor functions for patients suffering from spinal cord injuries, amputations, and other conditions. The efficacy of these neural interfaces, and the precision of sensory activity decoding is heavily reliant on artificial intelligence techniques at the backend. Traditional machine learning techniques, such as feature-based classification, generally requires extensive feature engineering and domain expertise, and is often ineffective at handling very high-dimensional data. Additionally, convolutional neural networks (CNNs) tend to perform best when employing floating-point operations, which inturn can be computationally intensive and less energy-efficient. Addressing these challenges, this paper presents a multiplier-less spiking neural network (SNN) that utilizes fewer neurons to achieve higher accuracy. Leveraging the discrete firing characteristics of SNNs, we propose a hardware implementation model, trained on experimentally recorded action potentials from rat peripheral nerves. This demonstrates a significant improvement in Macro F1-score, reaching 0.89, over various CNNs while using $\mathbf{9 9. 8 \%}$ fewer parameters. It fully decodes three degrees of rat motion (dorsiflexion, plantarflexion, and pricking stimulation), showcasing the potential for efficient and accurate hardware integration. This work highlights a path towards the development of next-generation hardware-assisted neural interfaces.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".