{"id":"W2593552556","doi":"10.1371/journal.pone.0173684","title":"Iterative free-energy optimization for recurrent neural networks (INFERNO)","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroDevNet","funders":"Agence Nationale de la Recherche; Centre National de la Recherche Scientifique; Equipex","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Artificial neural network; Content-addressable memory; Gradient descent; Neuroscience; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.00005876904,0.0001124828,0.0001357815,0.00003494563,0.0005635282,0.0002658375,0.0003253062,0.00005044423,0.00003785373],"category_scores_gemma":[0.0007390162,0.000101828,0.00005026984,0.00004440095,0.00005906262,0.0003451009,0.0001169015,0.00009342695,0.000002254094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002514048,"about_ca_system_score_gemma":0.000007460089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009322814,"about_ca_topic_score_gemma":0.00001861869,"domain_scores_codex":[0.999114,0.00004144101,0.000140899,0.0003151026,0.00018049,0.0002080352],"domain_scores_gemma":[0.9991444,0.0001238464,0.000163494,0.0004347215,0.00007389132,0.00005964792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002086289,0.006629866,0.003402333,0.0003078106,0.0002207889,0.00005038302,0.0004442217,0.3709054,0.3964311,0.09866459,0.00772821,0.113129],"study_design_scores_gemma":[0.0004078338,0.0002721055,0.0001891492,0.00003533922,0.00002207925,8.838024e-7,0.000001252622,0.980841,0.01726173,0.0007465253,0.00009938375,0.0001227089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6811845,0.00009250066,0.2980089,0.009955083,0.002859083,0.00164643,0.0002269675,0.0003616606,0.005664832],"genre_scores_gemma":[0.9963448,0.00007807979,0.001309466,0.0006823553,0.0003709308,0.00008552665,0.00002775276,0.0000216034,0.001079495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6099356,"threshold_uncertainty_score":0.4334261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07473026143926585,"score_gpt":0.2579853049081127,"score_spread":0.1832550434688469,"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."}}