{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004028876,0.00007601234,0.0001612382,0.0001961263,0.00004509489,0.00002578146,0.0001093161,0.00004163399,7.419532e-7],"category_scores_gemma":[0.00008407959,0.00005964207,0.00005092529,0.0001726747,0.00003278812,0.0004844742,0.00002216552,0.0002221371,0.000009073463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003911429,"about_ca_system_score_gemma":0.00004467228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004402871,"about_ca_topic_score_gemma":4.761571e-7,"domain_scores_codex":[0.9991326,0.00007965704,0.0004314574,0.00008423061,0.0001361306,0.000135866],"domain_scores_gemma":[0.9994492,0.00004137068,0.0003506707,0.00006386939,0.00007095994,0.00002394888],"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":[0.00008092038,0.00003212068,0.00000702182,0.00003168956,2.14925e-7,0.00004964517,0.0000165542,0.1540131,0.8443236,0.0003855903,0.00001813677,0.001041399],"study_design_scores_gemma":[0.000388785,0.0002085088,0.001522108,0.00003398898,0.000003685568,0.0003716794,0.00001722035,0.9894192,0.007218338,0.0004749123,0.0002743926,0.00006714538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978291,0.000003432112,0.001063727,0.0001499646,0.0002782638,0.00005342961,0.000003052251,0.00001482134,0.0006042056],"genre_scores_gemma":[0.9990013,0.000008712778,0.0003100056,0.0004810599,0.0001411133,7.462097e-7,6.969307e-7,0.000006443208,0.00004996294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8371053,"threshold_uncertainty_score":0.2432133,"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."}}