{"id":"W2021394691","doi":"10.1162/neco.2007.19.2.404","title":"Fast Population Coding","year":2007,"lang":"en","type":"letter","venue":"Neural Computation","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Neural coding; Encoder; Computer science; Decoding methods; Stimulus (psychology); Computation; Population; ENCODE; Theoretical computer science; Models of neural computation; Coding (social sciences); Artificial intelligence; Artificial neural network; Algorithm; Neuroscience; Psychology; Mathematics; Cognitive psychology; Biology","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":[0.0001093946,0.0002857408,0.0002333583,0.000310135,0.000238151,0.0001800961,0.0001733341,0.0003988957,0.00003182273],"category_scores_gemma":[0.0001336059,0.0002801635,0.0001230073,0.0003603988,0.00003883879,0.0002620316,0.00005239303,0.00119256,0.0001035617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001275208,"about_ca_system_score_gemma":0.00001093655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004236097,"about_ca_topic_score_gemma":0.000005231707,"domain_scores_codex":[0.9978731,0.0001673055,0.000372016,0.0006288036,0.0005896821,0.0003690747],"domain_scores_gemma":[0.9990305,0.0003993388,0.0003114154,0.0001618591,0.00005615275,0.00004078899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005004858,0.00003193052,0.0001774499,0.0001774023,0.000007507562,0.0006524646,0.00006408046,0.01188776,0.03160921,0.0003337938,0.8621455,0.09286288],"study_design_scores_gemma":[0.0007994255,0.0003787133,0.006001696,0.0001360496,0.00005304251,0.0003899776,0.000008237005,0.8242021,0.00474597,0.004317121,0.1575536,0.001414135],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.2305589,0.00003192246,0.03743412,0.7054601,0.01544838,0.001879709,0.0001230372,0.001551948,0.00751189],"genre_scores_gemma":[0.4452999,0.000006326189,0.0001190349,0.5504991,0.002479387,0.000006656875,0.0006963745,0.00005821181,0.0008349619],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.8123143,"threshold_uncertainty_score":0.9999651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04796961973270329,"score_gpt":0.2877586968825501,"score_spread":0.2397890771498468,"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."}}