Low power programmable front-end for a multichannel neural recording interface
Bibliographic record
Abstract
In this paper a programmable gain preamplification front-end for a fully implantable multichannel data acquisition system (IMDAS) that is dedicated for chronic neural signal recording is proposed. This application calls for very low power and low voltage circuit techniques. To satisfy these constraints, the preamplifier is designed in 0.18 /spl mu/m CMOS technology and all employed transistors operate in weak inversion. To maximize the dynamic range of the recorded signals, the preamplifier's gain is set by a 4-bit digital-to-analog converter (DAC). This DAC is used to tune the bias currents of a variable transconductor cell in order to vary its DC gain. The simulation of the whole proposed module gives a maximum power consumption of 530 nW at a supply voltage of 0.9 V. The circuit provides a maximum gain of 47 dB, has a cutoff frequency of 2.65 KHz and presents an input-referred noise of 7.65 /spl mu/Vrms which is sufficiently low to meet the required precision. In addition, the phase margin is higher than 50/spl deg/ on the entire gain range.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".