{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008560222,0.0001365796,0.0001962945,0.00002442413,0.0000714883,0.0000574969,0.0001097667,0.00003613024,0.00003111777],"category_scores_gemma":[0.00001563897,0.0001522364,0.00002712644,0.0002823432,0.00002086063,0.00008337864,0.00004272405,0.0001036214,0.00003464054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002359751,"about_ca_system_score_gemma":0.00001645943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001809309,"about_ca_topic_score_gemma":0.000004275735,"domain_scores_codex":[0.999177,0.00004623003,0.0002866972,0.0001982816,0.00007698493,0.000214762],"domain_scores_gemma":[0.9995718,0.0001361431,0.00004111771,0.0001875227,0.00002983113,0.00003360008],"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":[0.000002734675,0.00003164469,0.00003583922,0.0001334159,0.000006957762,0.00002089438,0.00005843109,0.3067305,0.6828843,0.0026048,0.00000146552,0.007489058],"study_design_scores_gemma":[0.00002976021,0.000007084207,0.0001728726,0.00005418147,0.000007116032,0.000002883263,0.00004541759,0.05924177,0.9358668,0.004350189,0.00006209336,0.0001598087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7903809,0.0000488289,0.208904,0.00002366235,0.0002848096,0.0001075591,0.000008419765,0.0001756916,0.0000661731],"genre_scores_gemma":[0.9979064,0.000003903139,0.001699323,0.00007697198,0.0002382637,0.00001127529,0.00003580366,0.0000263794,0.00000165032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2529826,"threshold_uncertainty_score":0.6208022,"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."}}