{"id":"W3012591046","doi":"10.1038/s41593-020-0607-9","title":"Recurrent architecture for adaptive regulation of learning in the insect brain","year":2020,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","cited_by":189,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Wellcome Trust; Howard Hughes Medical Institute","keywords":"Mushroom bodies; Neuroscience; Optogenetics; Associative learning; Sensory system; Computer science; Dopaminergic; Biological neural network; Population; Connectome; Biology; Drosophila melanogaster; Functional connectivity; Dopamine","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":[0.000337541,0.0002292783,0.000436761,0.0001871623,0.0002460716,0.000782136,0.0007448253,0.0005408486,0.001660613],"category_scores_gemma":[0.001358017,0.0002319632,0.0004234536,0.0001578218,0.0005925789,0.0008010491,0.0005209571,0.0007297717,0.0002131211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005776106,"about_ca_system_score_gemma":0.0003255888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001737822,"about_ca_topic_score_gemma":0.002240223,"domain_scores_codex":[0.9998555,0.00003344914,0.000007140176,0.00004341792,0.00002388365,0.00003657732],"domain_scores_gemma":[0.999652,0.0001321079,0.00005826193,0.00004465087,0.00006922569,0.00004372423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002983727,0.0001127785,0.003737517,0.0001442974,0.0002137457,0.000276073,0.0002960283,0.5771252,0.1466112,0.179603,0.003146996,0.08843482],"study_design_scores_gemma":[0.000009401988,0.00003705386,0.001473037,0.000004512805,0.00001503081,0.00003441319,0.00001268087,0.9514764,0.002183037,0.04440335,0.0003372547,0.00001386942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4123175,0.0007608746,0.5753424,0.0009768147,0.0001467121,0.00002380284,0.0001835403,0.0008423271,0.009406019],"genre_scores_gemma":[0.9870848,0.0001354116,0.01032419,0.00005576714,0.00003462779,0.00001624964,0.00005212654,0.00005285146,0.002243986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001737822,"threshold_uncertainty_score":0.005555332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05974485142780819,"score_gpt":0.3241659008608339,"score_spread":0.2644210494330257,"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."}}