{"id":"W2964290236","doi":"10.1098/rsta.2018.0129","title":"Geometric analysis of synchronization in neuronal networks with global inhibition and coupling delays","year":2019,"lang":"en","type":"article","venue":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","topic":"Nonlinear Dynamics and Pattern Formation","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Excitatory postsynaptic potential; Inhibitory postsynaptic potential; Coupling (piping); Population; Synchronization (alternating current); Neuroscience; Synchronization networks; Physics; Nonlinear system; Computer science; Control theory (sociology); Mathematics; Topology (electrical circuits); Biology; Materials science; Artificial intelligence; Combinatorics","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":[0.0003621567,0.0006008326,0.0003846588,0.0009627407,0.0002673296,0.000618472,0.0006771816,0.0005069944,0.00142508],"category_scores_gemma":[0.001804268,0.0002452404,0.0004115991,0.0004536315,0.001192048,0.0009465845,0.0009901022,0.0003846313,0.0001660087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009055453,"about_ca_system_score_gemma":0.0003386243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001481531,"about_ca_topic_score_gemma":0.0008036711,"domain_scores_codex":[0.9998442,0.00004719921,0.000006758547,0.00003458764,0.00004234224,0.00002485822],"domain_scores_gemma":[0.9993697,0.0002515481,0.0002222953,0.00002548155,0.00008117396,0.00004982598],"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":[0.00003703674,0.00001125772,0.0009325441,0.00005591864,0.0000381241,0.0001937764,0.000116353,0.7893724,0.009311787,0.1939376,0.0004074957,0.005585799],"study_design_scores_gemma":[0.000007005975,0.00002927626,0.0004066811,0.000005022612,0.000008535607,0.00005269973,0.0000305055,0.9463428,0.000610206,0.05194007,0.0005581561,0.000008999694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4491055,0.0009594046,0.5356333,0.0006267345,0.00005574178,0.00004805947,0.0001179238,0.0001636373,0.0132898],"genre_scores_gemma":[0.9848643,0.0004794249,0.01187954,0.00004191084,0.00004998894,0.00004872,0.00006159878,0.00005136797,0.002523094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001481531,"threshold_uncertainty_score":0.00657022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007281028453414082,"score_gpt":0.2040153856765683,"score_spread":0.1967343572231542,"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."}}