{"id":"W3011624340","doi":"10.1016/j.cub.2020.01.030","title":"Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals","year":2020,"lang":"en","type":"letter","venue":"Current Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial neural network; Biology; Nervous system network models; Artificial intelligence; Layer (electronics); Physical neural network; Types of artificial neural networks; Computer science; Time delay neural network; Self-organization; Nanotechnology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008142835,0.0006844733,0.0007938285,0.0000878823,0.0003375624,0.0003655227,0.002304585,0.0005637692,0.00004079016],"category_scores_gemma":[0.00001830439,0.0005948946,0.0002926204,0.0009627788,0.00007667036,0.0001196825,0.001030617,0.001915521,0.0001943212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007535926,"about_ca_system_score_gemma":0.00006892178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003326868,"about_ca_topic_score_gemma":0.00002518328,"domain_scores_codex":[0.9959279,0.0004943165,0.0004796439,0.001689573,0.0001992732,0.001209312],"domain_scores_gemma":[0.9978302,0.0003211547,0.0003298269,0.001067278,0.0001178868,0.0003336297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000477744,0.00005707983,0.000320683,0.00001102453,0.00009318424,0.00008654357,0.00006345068,0.006294502,0.0002808779,0.0001045374,0.9732916,0.01939176],"study_design_scores_gemma":[0.0001965895,0.0001054289,0.0000520717,0.00002686812,0.00004655198,0.000006986321,0.000001344977,0.3066129,0.00001631151,0.0002311749,0.6921529,0.0005508823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0006143565,0.006048219,0.4857898,0.4996482,0.005758853,0.001100867,0.0002374506,0.0007888831,0.00001339198],"genre_scores_gemma":[0.0397731,0.0008818408,0.01941399,0.8819009,0.05062604,0.0008502303,0.006053345,0.0001828655,0.0003177069],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.4663758,"threshold_uncertainty_score":0.9996502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07146060198656526,"score_gpt":0.2914345708567242,"score_spread":0.2199739688701589,"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."}}