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Record W2791945743 · doi:10.1088/1741-2552/aaa904

Cortical visual prostheses: from microstimulation to functional percept

2018· review· en· W2791945743 on OpenAlexafffund
Armin Najarpour Foroushani, Christopher C. Pack, Mohamad Sawan

Bibliographic record

VenueJournal of Neural Engineering · 2018
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhospheneMicrostimulationVisual cortexPerceptVisual prosthesisNeuroscienceVisual perceptionPerceptionMacaqueComputer sciencePsychologyStimulationTranscranial magnetic stimulation

Abstract

fetched live from OpenAlex

Cortical visual prostheses are intended to restore vision by targeted electrical stimulation of the visual cortex. The perception of spots of light, called phosphenes, resulting from microstimulation of the visual pathway, suggests the possibility of creating meaningful percept made of phosphenes. However, to date electrical stimulation of V1 has still not resulted in perception of phosphenated images that goes beyond punctate spots of light. In this review, we summarize the clinical and experimental progress that has been made in generating phosphenes and modulating their associated perceptual characteristics in human and macaque primary visual cortex (V1). We focus specifically on the effects of different microstimulation parameters on perception and we analyse key challenges facing the generation of meaningful artificial percepts. Finally, we propose solutions to these challenges based on the application of supervised learning of population codes for spatial stimulation of visual cortex.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.095
GPT teacher head0.346
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations64
Published2018
Admission routes2
Has abstractyes

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