{"id":"W4361818145","doi":"10.1101/2023.03.30.534278","title":"Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Simple cell; Receptive field; Visual cortex; Computer science; Binocular neurons; Biological system; Parameterized complexity; Artificial intelligence; Simple (philosophy); Convolutional neural network; Pattern recognition (psychology); Neuroscience; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005922091,0.0004473807,0.0003172084,0.0002863879,0.0001367737,0.0003907554,0.0008286574,0.000658021,0.0006137538],"category_scores_gemma":[0.00138016,0.0003732078,0.0005266264,0.0002990678,0.0003853152,0.0004749899,0.0003603474,0.0007099072,0.0001384379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001203411,"about_ca_system_score_gemma":0.0005734912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01821519,"about_ca_topic_score_gemma":0.01207965,"domain_scores_codex":[0.999916,0.00001655864,0.000004429144,0.00002915907,0.00001563131,0.00001830304],"domain_scores_gemma":[0.9996461,0.0002008408,0.00005183414,0.00003478885,0.00004678437,0.00001960837],"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":[0.00006235304,0.00002667892,0.002013488,0.00001688656,0.00002687404,0.00004424914,0.00001795417,0.9791073,0.004986893,0.001376461,0.000261292,0.01205962],"study_design_scores_gemma":[9.25036e-7,0.000001787047,0.0001768117,6.313006e-7,0.000001150878,0.000002603905,5.197213e-7,0.9993321,0.0002383827,0.0002308039,0.00001310641,0.000001132774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4109586,0.0002963772,0.5865156,0.0004234328,0.00002481351,0.00004697384,0.0003146821,0.0003867132,0.001032685],"genre_scores_gemma":[0.9731295,0.00008044845,0.02499087,0.00004082477,0.000008296935,0.00003456358,0.0001604649,0.00002597268,0.001528975],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01821519,"threshold_uncertainty_score":0.03621829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04343232222059189,"score_gpt":0.2559723108514763,"score_spread":0.2125399886308844,"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."}}