{"id":"W2977381957","doi":"10.1162/neco_a_01292","title":"Inference of a Mesoscopic Population Model from Population Spike Trains","year":2020,"lang":"en","type":"preprint","venue":"Neural Computation","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Markov chain Monte Carlo; Inference; Computer science; Population; Bayesian inference; Mesoscopic physics; Bayesian probability; Sampling (signal processing); Algorithm; Statistical physics; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006987985,0.0003209563,0.0004420357,0.0001709752,0.0001052241,0.0001070488,0.0002634414,0.0002423171,0.00001568517],"category_scores_gemma":[0.0003785483,0.0003325773,0.0001622371,0.0002662444,0.00003562102,0.0002688919,0.0002585656,0.0005371175,0.000008373134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001010031,"about_ca_system_score_gemma":0.00004587938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008129718,"about_ca_topic_score_gemma":0.00006032881,"domain_scores_codex":[0.9975342,0.0002102433,0.0006707284,0.0008424995,0.0005456582,0.0001966864],"domain_scores_gemma":[0.9986346,0.0002467443,0.0006902476,0.0002565355,0.00008302474,0.00008880756],"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":[0.00006879074,0.00005099596,0.002269273,0.0001288258,0.000006511289,0.000003941434,0.0002509759,0.9012997,0.07741393,0.001521932,0.00003769832,0.01694742],"study_design_scores_gemma":[0.0002473306,0.00008001977,0.07988128,0.0000701176,0.00003383081,8.809822e-7,0.000004138996,0.8427765,0.002308717,0.07437366,0.000001505224,0.0002219759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9319181,0.00001217406,0.06531059,0.0006468638,0.0009813416,0.0005821407,0.0002489803,0.0001929465,0.0001068465],"genre_scores_gemma":[0.995878,0.00001211218,0.001916332,0.0004898326,0.0001594268,0.00003099582,0.001456048,0.00003824354,0.00001896826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07761201,"threshold_uncertainty_score":0.9999126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.097743939744386,"score_gpt":0.3263051032254121,"score_spread":0.2285611634810261,"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."}}