{"id":"W2027316195","doi":"10.1016/s0925-2312(01)00679-8","title":"Dynamic range and error tolerance of stochastic neural rate codes","year":2002,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Unary operation; Range (aeronautics); Artificial neural network; Algorithm; Computer science; Noise (video); Stochastic computing; Integer (computer science); Mathematics; Discrete mathematics; Artificial intelligence","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":[0.00007773473,0.00010832,0.0001524185,0.00004005843,0.0001354282,0.00005867836,0.000348502,0.00002197855,0.000003481884],"category_scores_gemma":[0.00001204835,0.0001026284,0.00003435421,0.0002709553,0.00004877527,0.0001371789,0.0001740077,0.0001232891,0.000007448864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004513375,"about_ca_system_score_gemma":0.000002807481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003661411,"about_ca_topic_score_gemma":0.000002017726,"domain_scores_codex":[0.9991341,0.00004543036,0.0002049186,0.0003087488,0.00009685709,0.0002099046],"domain_scores_gemma":[0.9993565,0.0001879386,0.0001108551,0.0002540043,0.0000319922,0.00005869316],"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":[0.00001595219,0.0003114823,0.001459326,0.0001779516,0.00002672378,0.00005813334,0.001865222,0.3366797,0.04985144,0.01528145,0.00138143,0.5928912],"study_design_scores_gemma":[0.0001841957,0.000034271,0.003666101,0.00001909478,0.000002992259,0.00002631706,0.000003300365,0.9956475,0.00007235283,0.0001710809,0.00008133111,0.0000914948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7585926,0.0002286391,0.2397055,0.0009411681,0.0001296709,0.0001726881,0.000001635966,0.0001124941,0.0001156189],"genre_scores_gemma":[0.99422,0.000006857955,0.005378075,0.0002916731,0.00003321694,0.000005997897,3.568119e-7,0.000008133886,0.00005567555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6589677,"threshold_uncertainty_score":0.4185066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089619510910429,"score_gpt":0.2452047672107638,"score_spread":0.2243085721016595,"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."}}