{"id":"W2543286693","doi":"10.1101/083626","title":"UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"European Regional Development Fund; Agència de Gestió d'Ajuts Universitaris i de Recerca; Ministerio de Economía y Competitividad; Generalitat de Catalunya","keywords":"Bistability; Neuroscience; Rhythm; State (computer science); Population; Excitatory postsynaptic potential; Physics; Dynamics (music); Inhibitory postsynaptic potential; Somatosensory system; State dependent; Network dynamics; Resting state fMRI; Statistical physics; Control theory (sociology); Computer science; Psychology; Mathematics; Quantum mechanics; Artificial intelligence; Medicine; Control (management)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001038232,0.0001888254,0.0001793977,0.0002379434,0.0001443101,0.0003903747,0.000255633,0.0002139159,0.001012954],"category_scores_gemma":[0.0004586623,0.0001253148,0.0001658813,0.0001219158,0.0003221913,0.0004634161,0.0002855735,0.0002198091,0.00009093012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003232007,"about_ca_system_score_gemma":0.000141136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308548,"about_ca_topic_score_gemma":0.001147729,"domain_scores_codex":[0.9999591,0.000007637515,0.00000208901,0.00001037071,0.000007769257,0.00001294946],"domain_scores_gemma":[0.999863,0.00003632865,0.00004184126,0.000014228,0.00001652039,0.00002803916],"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":[0.0002488241,0.0000839323,0.01647617,0.00005943897,0.0001016553,0.00066721,0.0002545232,0.3556799,0.5846437,0.02392339,0.0004442886,0.01741695],"study_design_scores_gemma":[0.000008504693,0.00005768105,0.01028112,0.000003906792,0.00001274707,0.00009667139,0.00004900062,0.9719927,0.009889049,0.007388153,0.0002096722,0.00001081442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640514,0.00003664343,0.03464981,0.00006234559,0.000003896331,0.000006655455,0.0000521896,0.00007558795,0.001061538],"genre_scores_gemma":[0.9987552,0.00001789745,0.0009569203,0.000006475899,0.00000126211,0.00000576003,0.00001806983,0.000005950927,0.0002324599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001308548,"threshold_uncertainty_score":0.003388643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179911465592425,"score_gpt":0.2353265338058234,"score_spread":0.2173353872465809,"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."}}