{"id":"W2546688631","doi":"10.3389/fncom.2016.00110","title":"Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; SickKids Foundation; University of Toronto","funders":"RIKEN Brain Science Institute; RIKEN; Fonds de Recherche du Québec - Santé; Japan Agency for Medical Research and Development","keywords":"Excitatory postsynaptic potential; Computer science; Inhibitory postsynaptic potential; Bayesian probability; Bayes' theorem; Inference; Artificial intelligence; Bayesian inference; Machine learning; Neuroscience; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0005835254,0.0001457026,0.0002954529,0.0002069979,0.0001423138,0.000043196,0.00018439,0.00004570056,0.000003626006],"category_scores_gemma":[0.009358344,0.0001139908,0.00004185543,0.0003691078,0.0004922148,0.0005192122,0.00004982743,0.00009170933,0.000001042009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004675883,"about_ca_system_score_gemma":0.00005601522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003695961,"about_ca_topic_score_gemma":0.000001020857,"domain_scores_codex":[0.9979191,0.0003492548,0.0005562638,0.0005580921,0.0003915019,0.0002257485],"domain_scores_gemma":[0.9958563,0.003561002,0.0003556687,0.0001056555,0.00004531865,0.00007601398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001027462,0.0000799578,0.002820245,0.00003166433,0.000001815839,0.00001862738,0.00006729648,0.05549578,0.9002306,0.0005576148,0.0007301741,0.03986341],"study_design_scores_gemma":[0.0008137329,0.0001581094,0.0009154099,0.00008303444,0.000004361909,0.00001924874,0.00004211441,0.9662557,0.02096345,0.01040601,0.0001549223,0.0001839543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6933994,0.00005387647,0.3044187,0.0003852918,0.001362139,0.0002609667,0.00005690135,0.00003748106,0.00002528284],"genre_scores_gemma":[0.9918693,0.00004677905,0.007556141,0.0004195853,0.00002389822,0.00001534645,0.000002493314,0.00001205729,0.00005435547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9107599,"threshold_uncertainty_score":0.9989862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419592386738719,"score_gpt":0.271176603537964,"score_spread":0.2369806796705768,"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."}}