A Highly Flexible System for Microstimulation of the Visual Cortex: Design and Implementation
Bibliographic record
Abstract
This paper presents the design of a system intended to be used as a prosthesis allowing profoundly visually impaired patients to recover partial vision by means of microstimulation in the primary visual cortex area. The main component of the system is a bio-electronic device to be implanted inside the skull of the user, composed of a plurality of stimulation modules, whose actions are controlled via an interface module. Power and data are transmitted to the implant wirelessly through a bidirectional inductive link, allowing diagnosis of the stimulating device and its environment after implantation, as well as power delivery optimization. A high level of flexibility is supported in terms of stimulation parameters, but a configurable communication protocol allows the device to be used with maximum efficiency. The core of an external controller implemented in a system on a programmable chip is also presented, performing data conversion and timing management such that phosphene intensity can be modulated by any parameter defining stimulation, either at the pulse level or in the time domain. Measured performances achieved with a prototype using two types of custom ASICs implemented in a 0.18-mum CMOS process and commercial components fulfill the requirements for a complete visual prosthesis for humans. When on/off activation is used with predefined parameters, stimuli measured on an electronic test bench could attain a rate in excess of 500 k pulses/s.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".