{"id":"W185261604","doi":"10.1023/a:1025860724292","title":"Noise-Stabilized Long-Distance Synchronization in Populations of Model Neurons","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada","keywords":"Synchronization (alternating current); Stability (learning theory); Noise (video); Theory of computation; Computer science; Excitatory postsynaptic potential; Coupling (piping); Neuroscience; Population; Biological system; Hippocampal formation; Inhibitory postsynaptic potential; Rhythm; Communication noise; Control theory (sociology); Physics; Artificial intelligence; Algorithm; Biology; Telecommunications; Machine learning","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":[0.0001871483,0.00006667076,0.000124966,0.0001381987,0.00005306754,0.00002224458,0.0001233274,0.00001144059,0.00001125758],"category_scores_gemma":[0.00008615401,0.00006332206,0.00005295893,0.00040804,0.00007773509,0.0002269649,0.00001012036,0.00009046503,4.006905e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000249304,"about_ca_system_score_gemma":0.0002386203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003411283,"about_ca_topic_score_gemma":0.000001646344,"domain_scores_codex":[0.9990253,0.00004109782,0.0004256746,0.0001045898,0.0003068586,0.0000964604],"domain_scores_gemma":[0.9991782,0.0000714562,0.0003890232,0.00006443224,0.0002541613,0.00004276302],"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":[0.000005348568,0.0001030348,0.01801533,0.000002702964,7.391529e-7,4.059929e-7,0.00004463045,0.8127801,0.0005981544,0.1681594,0.000008115637,0.0002820302],"study_design_scores_gemma":[0.0003302695,0.00003750698,0.06235917,0.0000178675,0.000005427882,0.000005399017,0.00001629511,0.8669552,0.00005849298,0.0701583,0.000005690337,0.00005042025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3404035,0.000007232469,0.6591036,0.00005932934,0.0001575536,0.00005004792,0.000006991048,0.000001414234,0.0002104185],"genre_scores_gemma":[0.9913759,7.870374e-7,0.008537279,0.00003652507,0.00002375885,8.229578e-7,0.000003405532,0.000005107771,0.00001638057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6509725,"threshold_uncertainty_score":0.2582199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0244600532575549,"score_gpt":0.2869515739693038,"score_spread":0.2624915207117489,"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."}}