{"id":"W3041927952","doi":"10.1101/2020.07.11.198564","title":"Bayesian Decoder Models with a Discriminative Observation Process","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Discriminative model; Encoder; State space; Artificial intelligence; Decoding methods; ENCODE; Encoding (memory); Bayesian inference; Dynamical systems theory; Autoencoder; Bayesian probability; Algorithm; Pattern recognition (psychology); Artificial neural network; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002258557,0.0009197767,0.001192422,0.0006403438,0.0003081103,0.00117099,0.001632041,0.001885133,0.00291689],"category_scores_gemma":[0.008398558,0.0008442815,0.0009780262,0.0006998569,0.001254993,0.001880836,0.001460633,0.00217237,0.0008164712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001237501,"about_ca_system_score_gemma":0.001069803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008160269,"about_ca_topic_score_gemma":0.007336073,"domain_scores_codex":[0.999203,0.0003053475,0.00003925111,0.0002277002,0.0001446027,0.00008004272],"domain_scores_gemma":[0.9963736,0.002604808,0.0003050136,0.0002987683,0.0003314568,0.00008627401],"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":[0.0001518,0.00005543733,0.001451922,0.00005476231,0.00006034146,0.0001013691,0.0001046423,0.86967,0.002626916,0.09197667,0.0009414934,0.03280477],"study_design_scores_gemma":[0.000006156416,0.000009135676,0.00009760234,0.000003649233,0.000004932343,0.00001348561,0.000002279915,0.9900635,0.0003148183,0.009318515,0.0001592741,0.000006615146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02029613,0.0001059124,0.977482,0.0004098946,0.00002725616,0.00002207255,0.0001803237,0.0002634775,0.00121281],"genre_scores_gemma":[0.8341222,0.0003154173,0.1512328,0.0003183147,0.0001129388,0.0002051863,0.0007425552,0.0001582834,0.01279235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008160269,"threshold_uncertainty_score":0.01622552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04491974581742732,"score_gpt":0.2441912551922651,"score_spread":0.1992715093748378,"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."}}