{"id":"W2793149949","doi":"10.1109/tvlsi.2018.2794445","title":"Toward an Energy-Efficient High-Voltage Compliant Visual Intracortical Multichannel Stimulator","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; CMC Microsystems","keywords":"Microelectrode; Multielectrode array; Channel (broadcasting); Electrical engineering; Electrical impedance; Computer science; Biomedical engineering; Materials science; Topology (electrical circuits); Physics; Electrode; Engineering","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":[0.0002630507,0.0003389562,0.0002349572,0.0001731465,0.0001352521,0.0003894592,0.0009971177,0.0004210397,0.001190903],"category_scores_gemma":[0.0003026238,0.0001889518,0.0002424221,0.0001682066,0.0001910733,0.0004754474,0.0005989427,0.0003584942,0.0006006194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002270447,"about_ca_system_score_gemma":0.0002682451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001708413,"about_ca_topic_score_gemma":0.000421104,"domain_scores_codex":[0.9997123,0.00003062716,0.00001704798,0.00008439336,0.0001320911,0.00002359968],"domain_scores_gemma":[0.9998103,0.00004734277,0.00004450874,0.00002790169,0.00004452941,0.0000254886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005857482,0.00003215125,0.0002867857,0.0001225712,0.00001736494,0.00007562267,0.00005665464,0.001325241,0.9514127,0.001724788,0.0003977488,0.04448983],"study_design_scores_gemma":[0.00004197866,0.0007293426,0.00245354,0.00002339943,0.00005138435,0.0009019327,0.00002489533,0.03279279,0.9360192,0.0008414774,0.02607248,0.00004761489],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1333123,0.001270392,0.8591204,0.0002515677,0.0001280437,0.0001236658,0.0001512952,0.001401058,0.004241382],"genre_scores_gemma":[0.4037648,0.0006301504,0.588348,0.0002420351,0.00008345267,0.0001592623,0.0001470572,0.0001688815,0.00645636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001190903,"threshold_uncertainty_score":0.003983974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03708551855367894,"score_gpt":0.2847868358480832,"score_spread":0.2477013172944042,"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."}}