{"id":"W2955274338","doi":"10.1162/neco_a_01211","title":"Approximating the Architecture of Visual Cortex in a Convolutional Network","year":2019,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visual cortex; Computer science; Convolutional neural network; Artificial intelligence; Heuristics; Hyperparameter; Network architecture; Cortex (anatomy); Retinotopy; Neuroscience; Pattern recognition (psychology); Psychology","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":[0.0001384811,0.00007473474,0.0001031714,0.00004405278,0.00006168493,0.0000204678,0.00008906469,0.00002841691,0.00001341373],"category_scores_gemma":[0.00007652385,0.00005314936,0.00003874454,0.0003302846,0.000045891,0.00007489965,0.00003873909,0.0001756344,0.00001100617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001731954,"about_ca_system_score_gemma":0.0000143484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000118348,"about_ca_topic_score_gemma":0.000007719544,"domain_scores_codex":[0.9990931,0.000140632,0.0002208561,0.0001924363,0.000207911,0.000145039],"domain_scores_gemma":[0.9993306,0.0004282429,0.0001402972,0.00006299678,0.00002268711,0.00001515826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000944729,0.00005323992,0.007659535,0.00003471103,0.000001988628,0.000002740011,0.0001824902,0.7276083,0.2377327,0.005114239,0.00005847099,0.02145714],"study_design_scores_gemma":[0.0002633737,0.000113702,0.06122387,0.00001540801,0.000001774695,0.00001557532,0.00001581914,0.933028,0.0008448981,0.004392641,0.00002682964,0.00005805148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946948,0.000008121397,0.003774614,0.0003487898,0.0004082962,0.0003055116,0.00000219241,0.00002421906,0.0004333992],"genre_scores_gemma":[0.9991491,7.421098e-7,0.0001799832,0.0005432838,0.00006433127,0.000005159477,0.000007988461,0.000006954092,0.0000424653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2368878,"threshold_uncertainty_score":0.2167368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501519532479735,"score_gpt":0.2573904911880346,"score_spread":0.2423752958632373,"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."}}