{"id":"W3170843322","doi":"10.1038/s41467-021-23901-7","title":"In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Centre Hospitalier Universitaire Sainte-Justine","funders":"Board of the Swiss Federal Institutes of Technology; École Polytechnique Fédérale de Lausanne","keywords":"In silico; Neuroscience; Cortical neurons; Computer science; Voltage-sensitive dye; Dynamics (music); Biological system; SIGNAL (programming language); Nerve net; Artificial intelligence; Electrophysiology; Biology; Physics","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":[0.0002003182,0.00008506329,0.0001212806,0.00006735637,0.0003038584,0.00002508992,0.0004634809,0.00007836097,0.00002502816],"category_scores_gemma":[0.00184333,0.00006988573,0.00007231264,0.0007570746,0.000180271,0.0001171858,0.000335401,0.0008861016,0.000006522989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007550451,"about_ca_system_score_gemma":0.00004565774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003002201,"about_ca_topic_score_gemma":0.002026361,"domain_scores_codex":[0.9988339,0.0003566415,0.0002950621,0.0001955595,0.0001764937,0.0001423221],"domain_scores_gemma":[0.9979117,0.0006335028,0.0001081069,0.001154267,0.0001593631,0.0000330935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008434438,0.0002431024,0.01244209,0.000006728445,0.000004912973,0.00001115826,0.0002068693,0.0001822987,0.2960331,0.6883706,0.001531823,0.0009588798],"study_design_scores_gemma":[0.0005897917,0.0000355414,0.3857841,0.0001035841,0.0000807752,0.00014389,0.001242672,0.5380676,0.02990212,0.03449207,0.009098321,0.0004594552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8204853,0.001258984,0.001891062,0.1644672,0.001492334,0.0006919685,0.000281895,0.00008537914,0.009345844],"genre_scores_gemma":[0.99682,0.0002186307,0.0004630017,0.00202272,0.00001910346,0.00001451245,0.00006768794,0.00001064315,0.000363756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6538785,"threshold_uncertainty_score":0.384972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03754718739191317,"score_gpt":0.3272041070258876,"score_spread":0.2896569196339745,"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."}}