{"id":"W2963138386","doi":"10.1167/19.4.29","title":"Using deep learning to probe the neural code for images in primary visual cortex","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Institutes of Health; U.S. National Library of Medicine; National Institute of General Medical Sciences; Canadian Institute for Advanced Research","keywords":"Unicode; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002698936,0.0004061199,0.0002288433,0.0003115854,0.0002017263,0.0004432449,0.0005360513,0.0005490729,0.001755641],"category_scores_gemma":[0.002373291,0.0002274147,0.000358705,0.000309482,0.0004818139,0.0009474859,0.0003451243,0.001631724,0.0004388991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016057,"about_ca_system_score_gemma":0.0005714794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009300206,"about_ca_topic_score_gemma":0.008421332,"domain_scores_codex":[0.9998499,0.00002048441,0.000003893478,0.00005035487,0.00003707755,0.00003827528],"domain_scores_gemma":[0.9996465,0.0001576682,0.00005744741,0.00004262982,0.00005875004,0.00003691387],"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":[0.0005346342,0.0002701199,0.02951717,0.0002772099,0.000182718,0.0003499131,0.0002155565,0.6355793,0.1491425,0.03617105,0.01235738,0.1354025],"study_design_scores_gemma":[0.000006732059,0.00002226548,0.006694207,0.000006641317,0.000005954493,0.00002615635,0.00001298974,0.9773288,0.007033056,0.00833055,0.0005235877,0.000009063339],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7370403,0.0006219035,0.2515391,0.00181096,0.0002034901,0.00003593256,0.001803785,0.001677451,0.005267105],"genre_scores_gemma":[0.9787275,0.0001717258,0.01769997,0.0001996564,0.00002890297,0.00002321732,0.0009369212,0.0001181521,0.002093886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009300206,"threshold_uncertainty_score":0.01849216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633690741515225,"score_gpt":0.3249912870791114,"score_spread":0.2986543796639591,"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."}}