{"id":"W2555865722","doi":"10.1101/081125","title":"Inferring Synaptic Excitation/Inhibition Balance from Field Potentials","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of California, San Diego","keywords":"Computation; Electrocorticography; Local field potential; Balance (ability); Macaque; Computer science; Neuroscience; Field (mathematics); Excitation; Spike (software development); Artificial intelligence; Statistical physics; Electroencephalography; Algorithm; Psychology; Physics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003809731,0.000339543,0.0003018608,0.0008884087,0.0001324254,0.0007951175,0.0002988506,0.0003575123,0.0006083327],"category_scores_gemma":[0.003316132,0.0001587032,0.000207169,0.0004694298,0.0002762717,0.0007151915,0.0002666474,0.0003585124,0.0002720788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002543882,"about_ca_system_score_gemma":0.0002544937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002026933,"about_ca_topic_score_gemma":0.002438576,"domain_scores_codex":[0.9999158,0.00002269886,0.00000719633,0.00002847803,0.00001560637,0.00001022071],"domain_scores_gemma":[0.9994973,0.0002806061,0.00008238718,0.00006213434,0.00004690346,0.0000306949],"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":[0.0004551703,0.0001750763,0.1732115,0.0003653666,0.0003121362,0.0007570594,0.0003175691,0.405944,0.2155603,0.01378885,0.002806864,0.1863061],"study_design_scores_gemma":[0.000009762987,0.00002900725,0.04829644,0.00001691873,0.00002002948,0.0002313468,0.00005980783,0.9272978,0.01052564,0.01300878,0.0004777735,0.00002690046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8558317,0.000243467,0.140547,0.0001689349,0.00001373064,0.00002403804,0.0007571405,0.0004499051,0.001964085],"genre_scores_gemma":[0.9881589,0.00009432377,0.01128739,0.00001209108,0.00001048024,0.000007792293,0.000281336,0.00002756213,0.000120274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002026933,"threshold_uncertainty_score":0.004030228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826613190126516,"score_gpt":0.2237971429189641,"score_spread":0.2055310110176989,"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."}}