A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration
Why this work is in the frame
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Bibliographic record
Abstract
We present a fully implantable, inductively powered optogenetic stimulator that enhances stimulation efficacy and pathway specificity while maximizing energy efficiency and channel-count scalability. By leveraging opsins' photon integration properties with raster scanning and Poisson-coded stimulation, we achieve a uniform power profile and reduce wiring complexity, enabling a scalable system that supports more stimulation channels without compromising safety or functionality, improving prosthetic vision resolution. We also employed a compact and power-efficient (0.026 and 1.02 W overhead) SNR-boosted ADC-less spike detection circuit to adapt each LED's light intensity based on real-time feedback from RGC spiking cells. This closed-loop adaptivity adjusts stimulation to opsin distribution variations, over time and across different patients, ensuring effective and consistent stimulation across patients, enhancing both energy efficiency and visual perception quality. The 3 3 IC, fabricated in 180nm CMOS, is coupled with a 100-channel custom optrode array fabricated using an InGaN process on a sapphire substrate. Experimental results demonstrate circuit-level performance, system-level efficacy, and in-vitro validation. Comparison tables highlight our work's advantages over state-of-the-art implantable spike detection systems and retinal prostheses.
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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