{"id":"W2320402719","doi":"10.1088/1742-6596/699/1/012012","title":"Graph structure modeling for multi-neuronal spike data","year":2016,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Japan Society for the Promotion of Science","keywords":"Spike (software development); Computer science; Scalability; Algorithm; Synthetic data; Graph; Spike train; Inversion (geology); Pattern recognition (psychology); Correlation; Artificial intelligence; Data mining; Theoretical computer science; Mathematics; 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":[0.0007600127,0.0009403438,0.0008846536,0.001953279,0.0005626394,0.0008462064,0.002198147,0.001469383,0.001591238],"category_scores_gemma":[0.004334184,0.0007327854,0.001245511,0.001720998,0.0006636996,0.00185107,0.001027288,0.001695928,0.0006974549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009625,"about_ca_system_score_gemma":0.001098293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009856076,"about_ca_topic_score_gemma":0.01670139,"domain_scores_codex":[0.9996046,0.0001061821,0.00001793753,0.0001149955,0.0001167157,0.00003959411],"domain_scores_gemma":[0.9982004,0.001084879,0.0002348292,0.0001984445,0.0001905752,0.00009078563],"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":[0.00003697033,0.00003520746,0.0009923164,0.00006448237,0.00007408299,0.0001096506,0.00005601172,0.9373582,0.002610264,0.02247697,0.001138372,0.0350475],"study_design_scores_gemma":[0.000001928634,0.000003920166,0.00009171385,0.000001851547,0.000002603653,0.000008715296,0.000002656109,0.9899493,0.0001918984,0.009554503,0.000187095,0.00000364717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007101645,0.00009875924,0.9918325,0.0001193579,0.00001597003,0.00001618697,0.000205247,0.0003651486,0.0002451552],"genre_scores_gemma":[0.5240524,0.0007127564,0.4674121,0.0002010958,0.0001363057,0.0003058318,0.003343087,0.0004070951,0.003429281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009856076,"threshold_uncertainty_score":0.01959741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1557515894036753,"score_gpt":0.3114992655948252,"score_spread":0.1557476761911499,"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."}}