Low-power adaptive spike detector based on a sigma-delta control loop
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a resources-optimized digital action potential (AP) detector featuring an adaptive threshold based on a new Sigma-delta control loop. The proposed AP detector is optimized for utilizing low hardware resources, which makes it suitable for implementation on most popular low-power microcontrollers units (MCU). The adaptive threshold is calculated using a digital control loop based on a Sigma-delta modulator that precisely estimates the standard deviation of the amplitude of the neuronal signal. The detector was implemented on a popular low-power MCU and fully characterized experimentally using previously recorded neural signals with different signal-to-noise ratios. A comparison of the obtained results with other thresholding approaches shows that the proposed method can compete with high performance and highly resources demanding spike detection approaches while achieving up to 100% of true positive detection rate at high SNR, and up to 63% for an SNR as low as 0 dB, while necessitating an execution time as low as 11 μs with the MCU operating at 8 MHz.
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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 it