{"id":"W2892242726","doi":"10.48550/arxiv.1810.11393","title":"Dendritic cortical microcircuits approximate the backpropagation algorithm","year":2018,"lang":"en","type":"preprint","venue":"Bern Open Repository and Information System (University of Bern)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Horizon 2020 Framework Programme; Natural Sciences and Engineering Research Council of Canada; European Commission; Canada Research Chairs; Canadian Institute for Advanced Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Backpropagation; Computer science; Neuroscience; Artificial neural network; Artificial intelligence; Deep learning; Biological neural network; Machine learning; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004271677,0.0001751378,0.0002815923,0.0000948386,0.0008889368,0.0004358082,0.0006920332,0.0001991342,0.00001467318],"category_scores_gemma":[0.00002488971,0.000159106,0.00008869682,0.0001281401,0.0002659218,0.001801188,0.001073351,0.0003009682,0.00005665084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082093,"about_ca_system_score_gemma":0.00009158226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002536416,"about_ca_topic_score_gemma":0.000003637354,"domain_scores_codex":[0.998556,0.0002714035,0.0003731158,0.0002987222,0.0003395753,0.0001612337],"domain_scores_gemma":[0.9985342,0.00009734989,0.0006874848,0.0003894496,0.0002132728,0.0000782493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004331385,0.0012199,0.01011716,0.034188,0.001482428,0.0006315281,0.09497857,0.002106027,0.275551,0.2685179,0.02477415,0.282102],"study_design_scores_gemma":[0.005400783,0.0008606888,0.03551803,0.003709282,0.0008490255,0.002695918,0.02780054,0.8449543,0.05255431,0.001067691,0.02211287,0.002476611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8951433,0.00002884827,0.0470444,0.0003923452,0.002507731,0.002625271,0.0002988354,0.0001454351,0.05181386],"genre_scores_gemma":[0.997877,0.00002464937,0.0004857081,0.0001230558,0.00007353142,0.000003740987,0.00005776911,0.000008607114,0.001345922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8428482,"threshold_uncertainty_score":0.6837073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059577636203979,"score_gpt":0.2106922147391168,"score_spread":0.190096438377077,"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."}}