{"id":"W3036420223","doi":"10.1038/s41598-020-67258-1","title":"Neuronal On- and Off-type heterogeneities improve population coding of envelope signals in the presence of stimulus-induced noise","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Deutsche Forschungsgemeinschaft; Compute Canada","keywords":"Stimulus (psychology); Neural coding; Neuroscience; Population; Sensory system; Perception; Pooling; Coding (social sciences); Biological system; Computer science; Biology; Artificial intelligence; Psychology; Mathematics; Cognitive psychology; Statistics; Medicine","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":[0.000352116,0.0003704948,0.0004519243,0.000188492,0.0001340509,0.0004344758,0.0004229739,0.0004561557,0.001179584],"category_scores_gemma":[0.001286257,0.0001768895,0.0003041667,0.0001098709,0.0003352021,0.0005646857,0.0007762371,0.0006062837,0.0001991882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001990926,"about_ca_system_score_gemma":0.000182251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000369753,"about_ca_topic_score_gemma":0.0006221312,"domain_scores_codex":[0.9998093,0.00002518423,0.00001393344,0.00004797795,0.00005120262,0.00005234692],"domain_scores_gemma":[0.9995329,0.0001786512,0.00006695395,0.00006685741,0.00006450872,0.00009020188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001538985,0.00005892618,0.0009572976,0.00003690627,0.00001593384,0.00004336454,0.00004141327,0.001006772,0.9862672,0.0002069005,0.00003708992,0.01117421],"study_design_scores_gemma":[0.00004612156,0.00105345,0.08813842,0.00004404091,0.0001976785,0.0004496862,0.0002564504,0.09665367,0.8078656,0.003183425,0.002068588,0.00004277132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814523,0.0002040839,0.01625886,0.00006185939,0.00002896672,0.00001547941,0.0000362631,0.00008446472,0.00185767],"genre_scores_gemma":[0.9943586,0.0001099171,0.00471884,0.00006510231,0.00001108282,0.00001661193,0.00004993715,0.00003371217,0.000636332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001179584,"threshold_uncertainty_score":0.003946066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0546880610301391,"score_gpt":0.2734067556853443,"score_spread":0.2187186946552052,"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."}}