33.5 Closed-Loop 100-Channel Highly-Scalable Retinal Implant with 1.02μW Analog ED-Based Adaptive-Threshold Spike Detection and Poisson-Coded Temporally Distributed Optogenetic Stimulation
Bibliographic record
Abstract
Intraocular stimulators have demonstrated promise in treating patients with retinal degeneration (e.g., age-related macular degeneration) by restoring visual input to the compromised retina. This is done by capturing images with an electronic photosensor and subsequently stimulating remaining retinal cells, (e.g., bipolar, ganglion), thus bypassing the dysfunctional photoreceptors. Figure 33.5.1 (top, left) shows sub-and epi-retinal electrical stimulators, with the former potentially offering more natural vision restoration and the latter being less invasive. Regardless, a common issue with reported electrical retinal stimulators is their lack of cell-type specificity, which results in stimulating both ON and OFF pathways in the retinal network, as illustrated by the simulation results shown in Fig. 33.5.1 (left). This leads to contradictory messages to the brain, resulting in constrained visual perception, regardless of spatial resolution. This, and the introduction of various promoter opsins that allow for specifically activating ON pathway cells, make optogenetic stimulators a more promising alternative as shown in Fig. 33.5.1 (left). Additionally, optical stimulators can be epi-retinally implanted (i.e., less invasive) while stimulating bipolar cells for more natural vision restoration, thanks to the retinal neural network’s light transparency.
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) | 0.006 | 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".