{"id":"W2069729717","doi":"10.1152/jn.00619.2004","title":"Using Heterogeneity to Predict Inhibitory Network Model Characteristics","year":2004,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Inhibitory postsynaptic potential; Computer science; Population; Network dynamics; Biological system; Coherence (philosophical gambling strategy); Network model; Neuroscience; Artificial intelligence; Mathematics; Biology; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00004902067,0.0001283163,0.0002593753,0.00008868695,0.0001163602,0.0000223649,0.0002151377,0.00005610547,0.000002711957],"category_scores_gemma":[0.0002332797,0.0001065723,0.0001159584,0.0001823811,0.00005940967,0.0001370141,0.00009595602,0.0002951908,0.00001054779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005750941,"about_ca_system_score_gemma":0.00007844578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001142657,"about_ca_topic_score_gemma":2.463194e-7,"domain_scores_codex":[0.9988422,0.000102653,0.0003998379,0.0002135395,0.0001795842,0.000262181],"domain_scores_gemma":[0.9992548,0.00006380018,0.0003083734,0.0001539708,0.00007757077,0.0001414475],"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":[0.00009793659,0.00003444727,0.00001699875,0.00000309177,0.000001947904,0.00007283909,0.000008391338,0.3882075,0.6112429,0.0001347443,0.00001819471,0.0001609922],"study_design_scores_gemma":[0.003157265,0.008134476,0.05223203,0.0002522859,0.000141172,0.003951093,0.000009450617,0.6647922,0.2422713,0.02220056,0.00180327,0.00105484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906173,0.000002577791,0.007226867,0.000238131,0.001789477,0.00007905676,0.000007569108,0.00001441968,0.00002461496],"genre_scores_gemma":[0.9941562,0.00002921249,0.001099212,0.0037391,0.0009432114,6.524553e-7,3.710799e-7,0.00001998053,0.000012108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3689716,"threshold_uncertainty_score":0.4345894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05682158208047804,"score_gpt":0.2831272438424283,"score_spread":0.2263056617619503,"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."}}