{"id":"W3034495101","doi":"10.1016/j.cnsns.2020.105399","title":"Dynamics of spiking map-based neural networks in problems of supervised learning","year":2020,"lang":"en","type":"article","venue":"Communications in Nonlinear Science and Numerical Simulation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Future Earth","funders":"Russian Science Foundation","keywords":"Computer science; Artificial neural network; Artificial intelligence; Spiking neural network; Perspective (graphical); Dynamics (music); Coupling (piping); Biological neuron model; Range (aeronautics); Key (lock); Supervised learning; Machine learning; Pattern recognition (psychology); Biological system; Physics; Biology","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":[0.0007838166,0.0002656638,0.0004952915,0.000524957,0.0004433733,0.00107624,0.001020251,0.001256928,0.002261385],"category_scores_gemma":[0.006812628,0.0003198994,0.0003631898,0.0004090352,0.001139123,0.001692061,0.001124959,0.000925313,0.0001889195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008137759,"about_ca_system_score_gemma":0.0005865713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002296259,"about_ca_topic_score_gemma":0.002051354,"domain_scores_codex":[0.99981,0.00007445909,0.000009245738,0.00003058544,0.00005023377,0.00002544262],"domain_scores_gemma":[0.9982546,0.001021332,0.000203163,0.00006697988,0.0002916525,0.0001623234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001020973,0.0000449923,0.001541026,0.0000993008,0.00004334462,0.00009608074,0.0001616229,0.7888629,0.003705119,0.1899565,0.001968399,0.01341867],"study_design_scores_gemma":[0.000003782957,0.000006631532,0.0001433439,0.000004443988,0.000001502979,0.00001282978,0.00001087697,0.9752787,0.0001339287,0.02426619,0.0001347874,0.000003017326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.508225,0.001449215,0.4623407,0.003611461,0.0002603887,0.0000788863,0.000249119,0.0002731566,0.02351214],"genre_scores_gemma":[0.9839437,0.000312129,0.0111894,0.00008401295,0.00004604737,0.00004153655,0.0000623478,0.00003737614,0.004283421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002296259,"threshold_uncertainty_score":0.007565022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03982318237303296,"score_gpt":0.2976528637170585,"score_spread":0.2578296813440256,"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."}}