{"id":"W2106162176","doi":"10.1109/tbcas.2007.893181","title":"Brain–Silicon Interface for High-Resolution <i>in vitro</i> Neural Recording","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto","funders":"CMC Microsystems","keywords":"Interface (matter); CMOS; Channel (broadcasting); Materials science; Brain–computer interface; Computer science; Electronic engineering; Optoelectronics; Neuroscience; Engineering; Electroencephalography; Telecommunications","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":[0.0001973846,0.0002590352,0.0002445935,0.0001628229,0.0001778572,0.0003429472,0.0008287383,0.000363236,0.00362377],"category_scores_gemma":[0.0003014414,0.0001691826,0.0001890124,0.0001793148,0.0001642525,0.0003645051,0.0003421872,0.0003513466,0.001334112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001968831,"about_ca_system_score_gemma":0.0002936997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003740865,"about_ca_topic_score_gemma":0.001048487,"domain_scores_codex":[0.9998696,0.00001377866,0.000010414,0.00003527714,0.00005590239,0.00001511752],"domain_scores_gemma":[0.9998319,0.00005129357,0.00002135273,0.00002371158,0.00005192057,0.00001973617],"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":[0.00006702601,0.00001961818,0.0002397199,0.00009010353,0.000008519801,0.00008142357,0.00002217115,0.0001307853,0.9803592,0.0004222759,0.0009588343,0.01760033],"study_design_scores_gemma":[0.0000382102,0.0005058448,0.002960289,0.00001572339,0.00004813676,0.001298266,0.00003437497,0.009738101,0.9668948,0.0004671109,0.01797853,0.00002051539],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3036256,0.002677524,0.676365,0.0005449838,0.0004972653,0.000232335,0.00145231,0.004386165,0.01021872],"genre_scores_gemma":[0.5639377,0.0009932437,0.4274777,0.0003694549,0.0001494965,0.0002886624,0.0009406079,0.0001968257,0.005646315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00362377,"threshold_uncertainty_score":0.01212275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03826681536031645,"score_gpt":0.2784513443041207,"score_spread":0.2401845289438043,"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."}}