{"id":"W3137431486","doi":"10.1016/j.mtphys.2021.100393","title":"Synaptic devices based neuromorphic computing applications in artificial intelligence","year":2021,"lang":"en","type":"article","venue":"Materials Today Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":267,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Memristor; Von Neumann architecture; Materials science; Computer science; Quantum computer; Unconventional computing; Computer architecture; Cognitive computing; Artificial neural network; Transistor; Nanotechnology; Spiking neural network; Artificial intelligence; Electronic engineering; Quantum; Neuroscience; Distributed computing; Electrical engineering; Engineering; Physics; Voltage; Cognition","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":[0.00007850442,0.0001718545,0.0002196924,0.0002363676,0.0002239733,0.0009434214,0.0006453398,0.0006336379,0.004981405],"category_scores_gemma":[0.0002859353,0.00009976674,0.000144656,0.0003337828,0.0004806249,0.0008275629,0.0003769471,0.000481512,0.0009853345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001822097,"about_ca_system_score_gemma":0.0001163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046048,"about_ca_topic_score_gemma":0.0002118056,"domain_scores_codex":[0.9999553,0.000007624859,0.000003661194,0.000007788545,0.00002011751,0.000005464875],"domain_scores_gemma":[0.999936,0.00002220661,0.000005351849,0.000009635442,0.00001957973,0.000007312816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009029867,0.00008947487,0.0002585993,0.0006256331,0.00002926708,0.0002740936,0.00009333626,0.004406728,0.2005844,0.6642669,0.007136881,0.1221444],"study_design_scores_gemma":[0.00005523581,0.0003374112,0.002014711,0.0002994751,0.0000995347,0.001402617,0.0002598887,0.1426858,0.2236208,0.4273638,0.2017965,0.00006416981],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.319005,0.04796605,0.2713665,0.007773714,0.005662,0.0001794047,0.0005964006,0.001471078,0.3459798],"genre_scores_gemma":[0.8935955,0.01105325,0.05180943,0.0006153753,0.0003832971,0.00007366649,0.0001458769,0.00008882637,0.04223483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004981405,"threshold_uncertainty_score":0.01666445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04305840218911718,"score_gpt":0.259886337811878,"score_spread":0.2168279356227608,"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."}}