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Record W3155864449 · doi:10.1088/1741-2552/abf892

Modelling the visual response to an OUReP retinal prosthesis with photoelectric dye coupled to polyethylene film

2021· article· en· W3155864449 on OpenAlexaff
Koichiro Yamashita, Prathima Sundaram, Tetsuya Uchida, Toshihiko Matsuo, Willy Wong

Bibliographic record

VenueJournal of Neural Engineering · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Toronto
FundersJapan Society for the Promotion of Science
KeywordsRetinal ProsthesisPhotoelectric effectRetinalPolyethyleneMaterials scienceRetinal implantVisual prosthesisRetinal detachmentProsthesisOphthalmologyComputer scienceOptoelectronicsMedicineArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

Abstract Objective. Retinal prostheses have been developed to restore vision in blind patients suffering from diseases like retinitis pigmentosa. Approach. A new type of retinal prosthesis called the Okayama University-type retinal prosthesis (OUReP) was developed by chemically coupling photoelectric dyes to a polyethylene film surface. The prosthesis works by passively generating an electric potential when stimulated by light. However, the neurophysiological mechanism of how OUReP stimulates the degenerated retina is unknown. Main results. Here, we explore how the OUReP affects retinal tissues using a finite element model to solve for the potential inside the tissue and an active Hodgkin–Huxley model based on rat vision to predict the corresponding retinal bipolar response. Significance. We show that the OUReP is likely capable of eliciting responses in retinal bipolar cells necessary to generate vision under most ambient conditions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.257
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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