{"id":"W2898304423","doi":"10.1016/j.neuron.2018.10.004","title":"Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain","year":2018,"lang":"en","type":"article","venue":"Neuron","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":262,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; European Research Council; Canadian Institute for Advanced Research; Commissariat à l'Énergie Atomique et aux Énergies Alternatives; Institut National de la Santé et de la Recherche Médicale; Collège de France; Fondation du Collège de France","keywords":"Predictive coding; Electrocorticography; Auditory cortex; Mean squared prediction error; Computer science; Sensory system; Prefrontal cortex; Artificial neural network; Temporal cortex; Neuroscience; Coding (social sciences); Artificial intelligence; Pattern recognition (psychology); Electroencephalography; Psychology; Machine learning; Mathematics; Cognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005118799,0.000264263,0.0003510794,0.0003841633,0.0004824396,0.001031509,0.0006801469,0.000609091,0.002502488],"category_scores_gemma":[0.004780347,0.0004285035,0.0004400837,0.0004403587,0.0009375043,0.00207613,0.0006945441,0.000814728,0.0002519322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008293852,"about_ca_system_score_gemma":0.0006130648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006535852,"about_ca_topic_score_gemma":0.00784922,"domain_scores_codex":[0.9998925,0.000032163,0.000004963673,0.00003314155,0.00001855218,0.00001863039],"domain_scores_gemma":[0.9991037,0.0005238309,0.0001164951,0.00008513792,0.00009579555,0.00007505188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001233095,0.00006761113,0.009050355,0.0001245863,0.0001435843,0.0002614384,0.000547534,0.7404608,0.022633,0.1576,0.002725854,0.06626198],"study_design_scores_gemma":[0.000006324319,0.0000133657,0.008009757,0.00000927317,0.00001386807,0.00006089494,0.00004705021,0.847249,0.001093072,0.1431734,0.00030776,0.00001632982],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5183998,0.00113741,0.4655057,0.003160744,0.00008893466,0.00002966843,0.0003419882,0.0006483028,0.01068741],"genre_scores_gemma":[0.9899526,0.0001781187,0.008538242,0.0000291061,0.00002074087,0.00001342999,0.00006606339,0.00003934995,0.001162349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006535852,"threshold_uncertainty_score":0.0129956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02122808318848253,"score_gpt":0.278341752241166,"score_spread":0.2571136690526835,"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."}}