Exponential Current Pulse Generation for Efficient Very High-Impedance Multisite Stimulation
Bibliographic record
Abstract
We describe in this paper an intracortical current-pulse generator for high-impedance microstimulation. This dual-chip system features a stimuli generator and a high-voltage electrode driver. The stimuli generator produces flexible rising exponential pulses in addition to standard rectangular stimuli. This novel stimulation waveform is expected to provide superior energy efficiency for action potential triggering while releasing less toxic reduced ions in the cortical tissues. The proposed fully integrated electrode driver is used as the output stage where high-voltage supplies are generated on-chip to significantly increase the voltage compliance for stimulation through high-impedance electrode-tissue interfaces. The stimuli generator has been implemented in 0.18-μm CMOS technology while a 0.8-μm CMOS/DMOS process has been used to integrate the high-voltage output stage. Experimental results show that the rectangular pulses cover a range of 1.6 to 167.2 μA with a DNL and an INL of 0.098 and 0.163 least-significant bit, respectively. The maximal dynamic range of the generated exponential reaches 34.36 dB at full scale within an error of ± 0.5 dB while all of its parameters (amplitude, duration, and time constant) are independently programmable over wide ranges. This chip consumes a maximum of 88.3 μ W in the exponential mode. High-voltage supplies of 8.95 and -8.46 V are generated by the output stage, boosting the voltage swing up to 13.6 V for a load as high as 100 kΩ.
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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".