{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002739562,0.0004886225,0.0004470497,0.0002330602,0.0002452001,0.0003462381,0.0004148929,0.0004732689,0.0001550736],"category_scores_gemma":[0.001339279,0.0004642586,0.0001794151,0.0002927267,0.00009152965,0.0003704854,0.0004431057,0.0006087369,0.0002571259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002004417,"about_ca_system_score_gemma":0.0001740349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005311508,"about_ca_topic_score_gemma":0.000002137478,"domain_scores_codex":[0.9968805,0.000222636,0.0005648111,0.001330182,0.0004872202,0.0005146799],"domain_scores_gemma":[0.9975884,0.0005014159,0.0005106804,0.0009997715,0.0002027315,0.00019703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002865848,0.00005049537,0.0008581735,0.00008037817,0.00002765032,0.00006601381,0.000003435608,0.00003810937,0.9962509,0.002350955,0.0002403035,0.000004960389],"study_design_scores_gemma":[0.0004997482,0.00006690408,0.02212211,0.0007401134,0.0000653869,3.034321e-8,7.688065e-7,0.002642683,0.9719347,0.0004509938,0.0006827544,0.0007937836],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734888,0.000115337,0.02023462,0.001066931,0.003527727,0.0005700585,0.000420596,0.0005309684,0.00004494889],"genre_scores_gemma":[0.9968256,0.0001744188,0.000601841,0.001360023,0.0007903409,0.0001372881,6.027907e-7,0.00009677323,0.000013077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02431614,"threshold_uncertainty_score":0.9997809,"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."}}