A Temperature-Aware Fully-Wireless mm-Scale Optically-Enhanced Optogenetic Neuro-Stimulator
Bibliographic record
Abstract
The design, development, and experimental validation of an inductively-powered 4-channel optical neuro-stimulator integrated circuit (IC) with on-chip neural recording, temperature monitoring, signal processing, and bidirectional wireless data communication are presented. Each stimulation channel employs a novel current amplifier that drives 0.1-10mA into the channel's dedicated µLED with only 150mV required headroom (smallest reported in literature). The amplifier yields a constant gain of 850A/A for the entire output current range, despite experiencing large supply voltage variations. The system's energy efficiency is further improved by inkjet printing of custom-designed optical µlenses on top of the device to enhance the generated light directivity. The chip is capable of simultaneous optical stimulation and LFP monitoring, that allows for on-the-fly stimulation parameter adjustment. A high-precision (0.1°C) temperature readout circuit is also integrated on the chip to shut off stimulation upon detection of an unsafe temperature increase. The IC is fabricated in a standard 130nm CMOS process and occupies 6mm2. Measurement results for different sensory/communication blocks are presented, as well as in-vitro experimental validation results showing simultaneous optical stimulation, electrical recording, and calcium imaging.
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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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".