A low-power current-reuse analog front-end for multi-channel neural signal recording
Bibliographic record
Abstract
Studying brain activity in-vivo requires to simultaneously record bioelectrical from several microelectrodes in order to capture neurons interactions. In this work, we present a new current-reuse analog front-end (AFE), which is scalable to very large number of recording channels, thanks to its small implementation area and its low-power consumption. This proposed AFE includes a low-noise amplifier (LNA) and a programmable gain amplifier (PGA) which employ fully differential folded cascode current-reuse structures leading to decreased power consumption and silicon area. Moreover, the proposed AFE presents improved output swing compared to previous current-reuse topologies by employing different common mode feedback circuits for LNA and PGA. A 4-channel system implemented in a CMOS 0.18-μm technology is presented as a proof-of-concept. Post-layout simulation results are reported to verify its performance. The total power consumption of one channel including a low-noise amplifier and a variable gain stage is 8.2 μW (4.1 μw for LNA and 4.1 μw for PGA), for an input referred noise of 3.28 μV. The entire AFE presents four selectable gains of 45.2 dB, 50.1 dB, 55.3 dB and 59.65 dB, and occupies a die area of 0.035 mm2per channel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".