MétaCan
Menu
Back to cohort
Record W7132940420

Response of Retinal Ganglion Cells to Electrical Stimulation: From Prosthesis to "Seeing'"

2020· dissertation· W7132940420 on OpenAlexfundno aff
Prathima Sundaram

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRetinal ProsthesisRetinal implantRetinalRetinaVisual prosthesisNeural ProsthesisFunctional electrical stimulation
DOInot available

Abstract

fetched live from OpenAlex

Electric stimulation is a promising method of restoring vision for those suffering from retinal degenerative diseases. To understand how retinal prostheses restore vision, computer models were developed to approximate the visual pathway. First, the electric potential generated by the subretinal implant within the retinal tissue was solved using Maxwell’s equation through finite-element method. Next, the primary neural response was calculated through the implementation of a recently proposed model of the retinal bipolar cell where the Hodgkin-Huxley equations and the cable model were solved for ten ionic currents. Finally, the entropy theory was introduced to calculate the ganglion response using the output of the bipolar response. For the first time, aspects of visual performance such as brightness and temporal response can be estimated for a subretinal implant. While these techniques have been developed to predict the biological response of any subretinal implant, much of the work presented here has been tailored towards a novel, passive implant developed by collaborators at Okayama University called the OUReP.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.043
GPT teacher head0.347
Teacher spread0.305 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicNeuroscience and Neural EngineeringFrench-language works237,207