Energy-Efficient Adaptive Neural Stimulator With Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface
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Bibliographic record
Abstract
This paper presents an implantable low-power neural stimulator that generates electrical stimulation pulses based on subject-specific edge-learning of electrode-tissue voltage profiles. The system deploys a low-magnitude constant-current stimulation pulse to create a training dataset, which is subsequently utilized to predict the desired electrode voltage waveforms for higher magnitudes of constant-current stimulation. The predicted waveform dataset has been used to control a custom switched-capacitor output stage, thereby avoiding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">driver_transistor</sub> · <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stimulation</sub> power loss as in the conventional neural stimulator drivers. The proposed system incorporates on-chip learning and prediction implemented within an ultra-low-power microcontroller, which has been optimized for memory- and power-constrained implantable environments. The stimulator output stage reduces power loss by up to 20% as compared to dynamic power supply scaling method, and consumes up to 3.63× lower as compared to conventional constant-current output stages. The intelligent neural interface system has been powered by a wireless inductive energy transfer link and is remotely controlled through a WiFi-based internet network. A custom-developed application interface, compatible with both mobile devices and personal computers, facilitates secure remote adjustments of stimulation parameters. The proposed system has been validated through a combination of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> rat peripheral nerve stimulation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> saline tests, and benchtop experiments. These results collectively demonstrate the potential to advance future neural implant technologies by enabling intelligence, safety, energy efficiency, and remotely controllable neural organ modulation.
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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 | 0.001 |
| 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