{"id":"W1559132467","doi":"10.1109/iscas.2015.7168700","title":"Hyperbolic tangent passive resistive-type neuron","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Hyperbolic function; Tangent; Artificial neural network; Standby power; Voltage; Neuron; Biological neuron model; Resistive touchscreen; CMOS; Function (biology); Computer science; Topology (electrical circuits); Control theory (sociology); Mathematics; Electronic engineering; Mathematical analysis; Artificial intelligence; Electrical engineering; Engineering; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001455451,0.0002813583,0.0002457371,0.000154675,0.0002541455,0.0005502066,0.001455175,0.0004698918,0.002391],"category_scores_gemma":[0.0002445914,0.0001655285,0.0002810104,0.000211081,0.0002432614,0.0007937416,0.000266487,0.0003045811,0.0009122886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004056196,"about_ca_system_score_gemma":0.000312702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006757005,"about_ca_topic_score_gemma":0.001215812,"domain_scores_codex":[0.9998685,0.00001527788,0.00001122508,0.00003740991,0.00005140195,0.00001618488],"domain_scores_gemma":[0.9998814,0.00001436377,0.00001234572,0.00001826725,0.00006555842,0.000007974952],"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":[0.000464739,0.000133999,0.001887597,0.000971079,0.0001377593,0.0008581101,0.0002835331,0.07110367,0.5979174,0.04127854,0.005177357,0.2797861],"study_design_scores_gemma":[0.00006190652,0.0009226943,0.001751014,0.00009405855,0.0001860412,0.001723348,0.00008948841,0.6648173,0.2793789,0.008926244,0.04198076,0.00006826096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07166572,0.001031916,0.8999038,0.0003605773,0.0003379603,0.000135817,0.000190585,0.002298736,0.02407493],"genre_scores_gemma":[0.8658224,0.0007413427,0.1117036,0.0002859955,0.0000462432,0.00007030739,0.0001402999,0.00006713002,0.02112273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002391,"threshold_uncertainty_score":0.007998705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04945330814562907,"score_gpt":0.2677537256034056,"score_spread":0.2183004174577765,"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."}}