Massively-Parallel Neuromonitoring and Neurostimulation Rodent Headset With Nanotextured Flexible Microelectrodes
Bibliographic record
Abstract
We present a compact wireless headset for simultaneous multi-site neuromonitoring and neurostimulation in the rodent brain. The system comprises flexible-shaft microelectrodes, neural amplifiers, neurostimulators, a digital time-division multiplexer (TDM), a micro-controller and a ZigBee wireless transceiver. The system is built by parallelizing up to four 0.35 μm CMOS integrated circuits (each having 256 neural amplifiers and 64 neurostimulators) to provide a total maximum of 1024 neural amplifiers and 256 neurostimulators. Each bipolar neural amplifier features 54 dB-72 dB adjustable gain, 1 Hz-5 kHz adjustable bandwidth with an input-referred noise of 7.99 μVrms and dissipates 12.9 μW. Each current-mode bipolar neurostimulator generates programmable arbitrary-waveform biphasic current in the range of 20-250 μA and dissipates 2.6 μW in the stand-by mode. Reconfigurability is provided by stacking a set of dedicated mini-PCBs that share a common signaling bus within as small as 22 × 30 × 15 mm³ volume. The system features flexible polyimide-based microelectrode array design that is not brittle and increases pad packing density. Pad nanotexturing by electrodeposition reduces the electrode-tissue interface impedance from an average of 2 MΩ to 30 kΩ at 100 Hz. The rodent headset and the microelectrode array have been experimentally validated in vivo in freely moving rats for two months. We demonstrate 92.8 percent seizure rate reduction by responsive neurostimulation in an acute epilepsy rat model.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".