A Low-Power Current-Reuse Analog Front-End for High-Density Neural Recording Implants
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Bibliographic record
Abstract
Studying brain activity in vivo requires collecting bioelectrical signals from several microelectrodes simultaneously in order to capture neuron interactions. In this work, we present a new current-reuse analog front-end (AFE), which is scalable to very large numbers of recording channels, thanks to its small implementation silicon area and its low-power consumption. This current-reuse AFE, which is including a low-noise amplifier (LNA) and a programmable gain amplifier (PGA), employs a new fully differential current-mirror topology using fewer transistors, and improving several design parameters, such as power consumption and noise, over previous current-reuse amplifier circuit implementations. We show that the proposed current-reuse amplifier can provide a theoretical noise efficiency factor (NEF) as low as 1.01, which is the lowest reported theoretical NEF provided by an LNA topology. A foue-channel current-reuse AFE implemented in a CMOS 0.18-μm technology is presented as a proof-of-concept. T-network capacitive circuits are used to decrease the size of input capacitors and to increase the gain accuracy in the AFE. The measured performance of the whole AFE is presented. The total power consumption per channel, including the LNA and the PGA stage, is 9 μW (4.5 μW for LNA and 4.5 μW for PGA), for an input referred noise of 3.2 μVrms, achieving a measured NEF of 1.94. The entire AFE presents three selectable gains of 35.04, 43.1, and 49.5 dB, and occupies a die area of 0.072 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> per channel. The implemented circuit has a measured inter-channel rejection ratio of 54 dB. In vivo recording results obtained with the proposed AFE are reported. It successfully allows collecting low-amplitude extracellular action potential signals from a tungsten wire microelectrode implanted in the hippocampus of a laboratory mouse.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it