A Low-Power Integrated Bioamplifier With Active Low-Frequency Suppression
Bibliographic record
Abstract
We present in this paper a low-power bioamplifier suitable for massive integration in dense multichannel recording devices. This bioamplifier achieves reduced-size compared to previous designs by means of active low-frequency suppression. An active integrator located in the feedback path of a low-noise amplifier is employed for placing a highpass cutoff frequency within the transfer function. A very long integrating time constant is achieved using a small integrated capacitor and a MOS-bipolar equivalent resistor. This configuration rejects unwanted low-frequency contents without the need for input RC networks or large feedback capacitors. Therefore, the bioamplifier high-input impedance and small size are preserved. The bioamplifier, implemented in a 0.18-mum CMOS process, has been designed for neural recording of action potentials, and optimised through a transconductance-ef-ficiency design methodology for micropower operation. Measured performance and results obtained from in vivo recordings are presented. The integrated bioamplifier provides a midband gain of 50 dB, and achieves an input-referred noise of 5.6 muVrms. It occupies less than 0.050 mm(2) of chip area and dissipates 8.6 muW.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".