{"id":"W3099546048","doi":"10.1101/2020.08.21.261198","title":"Suppression without inhibition: How retinal computation contributes to saccadic suppression","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Retinal Development and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Max-Planck-Gesellschaft; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Receptive field; Neuroscience; Saccadic masking; Retina; Surround suppression; Retinal waves; Parasol cell; Macaque; Photic Stimulation; Eye movement; Calcium imaging; Retinal; Retinal ganglion cell; Psychology; Biology; Visual perception; Perception; Intrinsically photosensitive retinal ganglion cells; Chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003033291,0.0005509634,0.0004739202,0.0001636707,0.0001991933,0.0002810805,0.0003528627,0.000646677,0.00001719291],"category_scores_gemma":[0.0003766315,0.0005696206,0.0001574037,0.0002924692,0.00009415371,0.00001501252,0.0008286941,0.0004523626,0.00004385927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007948979,"about_ca_system_score_gemma":0.000414763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007741128,"about_ca_topic_score_gemma":0.00000137462,"domain_scores_codex":[0.9973685,0.0001764978,0.0004151528,0.001189543,0.0003797221,0.0004705882],"domain_scores_gemma":[0.9982771,0.00001930804,0.0003151701,0.0005708864,0.0004746378,0.0003428484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000297202,0.00005820945,0.01349477,0.0002162375,0.00007974444,0.00001805726,0.000007844324,0.00009923603,0.965162,0.00004985748,0.02051259,0.000004194149],"study_design_scores_gemma":[0.0009984692,0.000172887,0.07708835,0.000642321,0.00007631243,3.854479e-8,0.000007789535,0.0002437308,0.8996542,0.0000104918,0.02029782,0.0008075907],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574192,0.001153665,0.03458577,0.004272558,0.001103548,0.001102388,0.0001631359,0.0001687388,0.00003100636],"genre_scores_gemma":[0.992573,0.0001871032,0.005674751,0.0008068923,0.0004482333,0.0001640999,0.00002792309,0.0001005165,0.00001754184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06550786,"threshold_uncertainty_score":0.9996755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163733588880214,"score_gpt":0.2323221651317714,"score_spread":0.2206848292429692,"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."}}