320-Channel Active Probe for High-Resolution Neuromonitoring and Responsive Neurostimulation
Bibliographic record
Abstract
We present a 320-channel active probe for high-spatial-resolution neuromonitoring and responsive neurostimulation. The probe comprises an integrated circuit (IC) cell array bonded to the back side of a pitch-matched microelectrode array. The IC enables up to 256-site neural recording and 64-site neural stimulation at the spatial resolution of 400 μ m and 200 μ m, respectively. It is suitable for direct integration with electrode arrays with the shank pitch of integer multiples of 200 μm. In the presented configuration, the IC is bonded with a 8 × 8 400 μ m-pitch Utah electrode array (UEA) and up to additional 192 recording channels are used for peripheral neuromonitoring. The 0.35 μ m CMOS circuit array has a total die size of 3.5 mm × 3.65 mm. Each stimulator channel employs a current memory for simultaneous multi-site neurostimulation, outputs 20 μA-250 μA square or arbitrary waveform current, occupies 0.02 mm (2), and dissipates 2.76 μ W quiescent power. Each fully differential recording channel has two stages of amplification and filtering and an 8-bit single-slope ADC, occupies 0.035 mm (2) , and consumes 51.9 μ W. The neural probe has been experimentally validated in epileptic seizure propagation studies in a mouse hippocampal slice in vitro and in responsive neurostimulation for seizure suppression in an acute epilepsy rat model in vivo .
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".