{"id":"W4385604105","doi":"10.1101/2023.08.01.551579","title":"Cell-type specific population codes link inferior temporal cortex to object recognition behavior","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Simons Foundation Autism Research Initiative; Simons Foundation","keywords":"Macaque; Neuroscience; Population; Inhibitory postsynaptic potential; Cognitive neuroscience of visual object recognition; Object (grammar); Categorization; Excitatory postsynaptic potential; Psychology; Pattern recognition (psychology); Biology; Artificial intelligence; Computer science","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":[0.000197678,0.000171486,0.0001596126,0.0001636125,0.0000963623,0.0005121354,0.0001860852,0.0002044094,0.001311497],"category_scores_gemma":[0.0013592,0.00009571446,0.0001466289,0.0001386634,0.0003222146,0.0003717347,0.0002948455,0.0002779597,0.000148915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003543815,"about_ca_system_score_gemma":0.000224457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002555666,"about_ca_topic_score_gemma":0.001923252,"domain_scores_codex":[0.9999413,0.000009782779,0.000004111591,0.00001879422,0.00001303185,0.00001296972],"domain_scores_gemma":[0.9996672,0.0001083507,0.00010223,0.00004024628,0.00005085617,0.00003113241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001358998,0.0000617644,0.02756975,0.00007258508,0.00005647451,0.0001097321,0.0001752731,0.05028562,0.8820267,0.008760089,0.0007212666,0.03002478],"study_design_scores_gemma":[0.00001288632,0.00009306512,0.175452,0.00001315315,0.00004743669,0.0001491726,0.0001270278,0.6599791,0.1477621,0.01533734,0.001002873,0.00002377794],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958931,0.0000740788,0.03682699,0.0001293356,0.00001576008,0.000009814703,0.0001911765,0.0001422718,0.003679693],"genre_scores_gemma":[0.9971383,0.00002504916,0.002243723,0.00001532753,0.00000372085,0.000004058625,0.00007199878,0.00002007488,0.0004778393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002555666,"threshold_uncertainty_score":0.005081594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05014460421577101,"score_gpt":0.2555267492174311,"score_spread":0.2053821450016601,"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."}}