{"id":"W1978907976","doi":"10.1103/physreve.79.011914","title":"Noise shaping in neural populations","year":2009,"lang":"en","type":"article","venue":"Physical Review E","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Consejo Nacional de Ciencia y Tecnología","keywords":"Excitatory postsynaptic potential; Information transmission; Renewal theory; Coupling (piping); Coupling strength; Stimulus (psychology); Train; Artificial neural network; Statistical physics; Biological system; Interval (graph theory); Computer science; Noise (video); Physics; Inhibitory postsynaptic potential; Mathematics; Neuroscience; Statistics; Artificial intelligence; Biology; Combinatorics","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":[0.0006914592,0.0002152074,0.0003817352,0.0004880734,0.0003095566,0.0008696396,0.0006206976,0.0006422523,0.00113145],"category_scores_gemma":[0.006601848,0.0002440433,0.0003646938,0.0002682472,0.0007860935,0.0009923097,0.0006514396,0.0004340752,0.0001864818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000905484,"about_ca_system_score_gemma":0.0003372853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001071113,"about_ca_topic_score_gemma":0.0005500632,"domain_scores_codex":[0.9996449,0.0001122491,0.00001656366,0.00008634001,0.00009306346,0.00004687962],"domain_scores_gemma":[0.9980262,0.001125843,0.0003373511,0.0001907874,0.0002066779,0.0001130934],"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":[0.0001683692,0.00005779354,0.004496075,0.0001432551,0.0000703918,0.0004125222,0.0004514806,0.6867724,0.06485968,0.2117677,0.0008903939,0.02990987],"study_design_scores_gemma":[0.00001491015,0.00003949246,0.002371883,0.00001242152,0.00001544233,0.0001201883,0.00004285951,0.9189617,0.00452713,0.073186,0.0006868338,0.00002115481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6297298,0.0007193093,0.353552,0.000581346,0.00005500305,0.00003748347,0.0001109362,0.0005173195,0.01469683],"genre_scores_gemma":[0.9935766,0.0001661392,0.005183091,0.00006102868,0.000014675,0.00002267388,0.00003191944,0.0000290685,0.0009148612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00113145,"threshold_uncertainty_score":0.006569803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.109489927054661,"score_gpt":0.3650640978075039,"score_spread":0.2555741707528429,"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."}}