{"id":"W4385971946","doi":"10.1088/2057-1976/acf1a5","title":"Learning to see via epiretinal implant stimulation in silico with model-based deep reinforcement learning","year":2023,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Axon; Retinal implant; Computer vision; Neuroscience; Retina; Pattern recognition (psychology); Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003816801,0.0005348934,0.0003790206,0.0001613611,0.0001609758,0.0004380473,0.0006718705,0.0008872126,0.001460564],"category_scores_gemma":[0.001730179,0.00023782,0.0004923189,0.00008756411,0.0006046518,0.0003097054,0.0005428657,0.0007338802,0.0001448506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005983393,"about_ca_system_score_gemma":0.0006034177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004222888,"about_ca_topic_score_gemma":0.00307159,"domain_scores_codex":[0.9998908,0.00003139922,0.000004603634,0.00002462387,0.0000265005,0.00002208133],"domain_scores_gemma":[0.9993062,0.0004489811,0.00009246443,0.00003574783,0.00007242148,0.0000441516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002628925,0.00002268844,0.0003959841,0.00001261189,0.000006364874,0.00002004229,0.00001060379,0.9957119,0.0007086004,0.0003564099,0.00005933555,0.002669166],"study_design_scores_gemma":[0.000003647627,0.00001791457,0.00004287481,0.000001285569,0.000001449884,0.000002454758,0.000001330452,0.9994957,0.0002125879,0.0001870883,0.00003249045,0.00000108734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5316895,0.0003197175,0.4579649,0.0006836954,0.0001070919,0.000159662,0.0001417618,0.0008932406,0.008040301],"genre_scores_gemma":[0.9805684,0.00003519078,0.01816708,0.000056855,0.000006572731,0.00006188012,0.00003805451,0.00001343248,0.001052501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004222888,"threshold_uncertainty_score":0.008396626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01724931151140622,"score_gpt":0.2489689433193001,"score_spread":0.2317196318078939,"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."}}