{"id":"W4361228079","doi":"10.1101/2023.03.28.534592","title":"A Retina-Inspired Computational Model for Stimulation Efficacy Characterization and Implementation Optimization of Implantable Optogenetic Epi-Retinal Neuro-Stimulators","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Optogenetics; Stimulation; Visual prosthesis; Stimulus (psychology); Retinal; Retinal implant; Visual perception; Computer science; Retina; Perception; Neuroscience; Biomedical engineering; Engineering; Psychology; Medicine; Ophthalmology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003094336,0.0003983027,0.000408977,0.000414037,0.0002397553,0.0001697205,0.0002895743,0.0001864529,0.000003489905],"category_scores_gemma":[0.0004797168,0.000468503,0.00009280112,0.0005675404,0.00009530166,0.000331892,0.0002416648,0.0002269878,0.000002123001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000816203,"about_ca_system_score_gemma":0.0002113865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005598802,"about_ca_topic_score_gemma":2.524176e-7,"domain_scores_codex":[0.9973161,0.00009121499,0.0006963157,0.001031064,0.0004448802,0.0004204919],"domain_scores_gemma":[0.9983497,0.0003195989,0.0005853751,0.0003718535,0.0002257684,0.0001477566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003783594,0.00002754217,0.0005190687,0.0001606359,0.000003957698,0.000001915804,0.000009518359,0.4794148,0.5196743,0.0001405413,0.000006196917,0.000003689148],"study_design_scores_gemma":[0.0005674724,0.00007502163,0.01719309,0.00008842441,0.00004518003,5.840754e-8,6.97959e-7,0.693422,0.2883145,0.000003901756,0.00001014628,0.0002795235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6843047,0.000006857133,0.3133692,0.00007667742,0.0003778525,0.001111971,0.0005251022,0.0002274584,2.439142e-7],"genre_scores_gemma":[0.9876526,0.00008599419,0.01175771,0.0001283397,0.00007485669,0.0001660784,0.00001094601,0.0001194329,0.000004025959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3033479,"threshold_uncertainty_score":0.9997767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03871867980818596,"score_gpt":0.2781553682414871,"score_spread":0.2394366884333012,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}