A Power Efficient Electronic Implant for a Visual Cortical Neuroprosthesis
Bibliographic record
Abstract
An integrated microstimulator designed for a cortical visual prosthesis is presented, along with a pixel reordering algorithm, together minimizing the peak total current and voltage required for stimulation of large numbers of electrodes at a high rate. In order to maximize the available voltage for stimulation at a given supply voltage for generating biphasic pulses, the device uses monopolar stimulation, where the return electrode voltage is dynamically varied. Thus, the voltage available for stimulation is maximized, as opposed to the conventional fixed return voltage monopolar approach, and impedance is significantly lower than can be achieved using bipolar stimulation with microelectrodes. This enables the use of a low voltage power supply, minimizing power consumption of the device. An important constraint resulting from this stimulation strategy, however, is that current generation needs to be simultaneous and in-phase for all active parallel channels, imposing heavy stress on the wireless power recovery and regulation circuitry in large electrode count systems such as a visual prosthesis. An ordering algorithm to be implemented in the external controller of the prosthesis is then proposed. Based on the data for each frame of the video signal to be transmitted to the implant, the algorithm minimizes the total generated current standard deviation between time multiplexed stimulations by determining the most appropriate combination of parallel stimulation channels to be activated simultaneously. A stimulator prototype has been implemented in CMOS technology and successfully tested. Execution of the external controller reordering algorithm on an application specific hardware architecture has been verified using a System-On-Chip development platform. A near 75% decrease in the total stimulation current standard deviation was observed with a one-pass algorithm, whereas a recursive variation of the algorithm resulted in a greater than 95% decrease of the same variable.
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".