A Bi-Directional Neural Interface Chip With 32-Channel 83-dB DR CTDSM-Based Recording Using FIRDAC With Pre-Emptive ELD Compensation
Bibliographic record
Abstract
Clinical-ready neuromodulation devices for real-time monitoring and treatment of neurological disorders require a neural interface integrated circuit (IC) that supports therapeutic neurostimulation together with concurrent recording of neural activities from large brain regions. To address this need, this article presents an IC with 32 high dynamic range (HDR) electrocorticography (ECoG) recording channels and two programmable neurostimulators allowing bi-directional neural interfacing. The recording channel uses a 2nd-order continuous-time$\Delta \Sigma $modulator (CTDSM) architecture with 12-tap finite impulse response digital-to-analog converter (FIRDAC) feedback facilitated by a pre-emptive current-steering excess loop delay (ELD) compensation scheme. By employing a high-performance linearized low-noise/low-power transconductance amplifier (L3TA) in the input integration stage, the recording channel achieves 83-dB dynamic range (DR) with up to 222-mVpplinear input range, and thus can accommodate large electrode dc offsets (EDOs) and stimulation artifacts. Thanks to the FIRDAC that relaxes component matching requirements, each recording channel occupies only 0.028 mm2and consumes$5.39~{\mu }$W while being manufactured in standard 180-nm CMOS technology. The concurrent neural recording and stimulation capabilities of the chip have been validated in a mouse experiment in vivo, which shows that the recording channel can tolerate real stimulation artifacts and the recorded ECoG signals can be separated from the artifacts with high fidelity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".