{"id":"W2157973185","doi":"10.1152/jn.01296.2005","title":"Nonlinear Information Processing in a Model Sensory System","year":2006,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Receptive field; Sensory system; Computer science; ENCODE; Neuroscience; Encoding (memory); Nonlinear system; Noise (video); Sensory stimulation therapy; Spike train; Artificial intelligence; Spike (software development); Physics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002978687,0.0003413074,0.0005489275,0.0003375785,0.0003987722,0.0008448988,0.0006148316,0.001184436,0.002447886],"category_scores_gemma":[0.001190612,0.0002338228,0.0004826671,0.0002689832,0.0007798025,0.001176453,0.0006370161,0.0004646405,0.0003534553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009706055,"about_ca_system_score_gemma":0.0007073285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004278148,"about_ca_topic_score_gemma":0.002586336,"domain_scores_codex":[0.999843,0.00003548882,0.000008467565,0.00004004403,0.00004592612,0.00002711399],"domain_scores_gemma":[0.9994751,0.0002809131,0.00006434499,0.0000425956,0.00009935731,0.00003774402],"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":[0.0002496137,0.00007478704,0.001070071,0.0001514773,0.00005351398,0.0004110152,0.0001704266,0.8999129,0.04133569,0.05089702,0.000524778,0.005148724],"study_design_scores_gemma":[0.00001659013,0.00002989039,0.000184912,0.000002748745,0.000006040891,0.00002770744,0.00001298778,0.9919428,0.001090914,0.006555003,0.0001224743,0.000008006926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7523168,0.0002609895,0.2277443,0.0009440801,0.00004628739,0.00008590415,0.0005045498,0.0003904365,0.01770681],"genre_scores_gemma":[0.9841676,0.0001096386,0.01106295,0.00006281449,0.0000130071,0.00006221514,0.00007028211,0.00001794912,0.00443351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004278148,"threshold_uncertainty_score":0.008506477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02021049931739209,"score_gpt":0.2398052826183807,"score_spread":0.2195947833009886,"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."}}