{"id":"W2131707961","doi":"10.1016/s0303-2647(02)00073-4","title":"Noise-induced divisive gain control in neuron models","year":2002,"lang":"en","type":"article","venue":"Biosystems","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noise (video); Context (archaeology); Multiplicative noise; Inhibitory postsynaptic potential; Gaussian noise; Electric fish; Shot noise; Biological system; Mathematics; Physics; Control theory (sociology); Computer science; Neuroscience; Algorithm; Transmission (telecommunications); Biology; Artificial intelligence; Telecommunications; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001240795,0.0001431998,0.0001912203,0.0001184621,0.00007348872,0.00006340906,0.000180283,0.0000709517,0.00002494847],"category_scores_gemma":[0.0001193766,0.0001241661,0.00005987357,0.000278578,0.00001794481,0.0002069833,0.00002733779,0.0001396979,0.0001731383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005153632,"about_ca_system_score_gemma":0.000005521552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007902965,"about_ca_topic_score_gemma":0.00002401752,"domain_scores_codex":[0.9986128,0.0001818739,0.0002604027,0.0004115144,0.0002394714,0.000293977],"domain_scores_gemma":[0.9994285,0.0001421737,0.00009265046,0.0002446912,0.00001858254,0.00007344546],"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":[0.00001594024,0.00007336026,0.0002846353,0.0000183424,0.00000136593,0.00005510173,0.00009605218,0.0008557244,0.9950965,0.001998843,0.0004020243,0.001102108],"study_design_scores_gemma":[0.001058761,0.0001659215,0.0006307194,0.00003857035,0.000003937908,0.0000286476,0.00002183728,0.979085,0.01766878,0.0002986839,0.0007821986,0.0002168929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917215,0.0000365627,0.0003606694,0.0007228334,0.001112577,0.0005333691,0.00002255177,0.00009719108,0.005392711],"genre_scores_gemma":[0.9981504,0.00001380315,0.000002279879,0.001058204,0.0001134157,0.00002943961,7.975617e-7,0.00001973108,0.0006119048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9782293,"threshold_uncertainty_score":0.5063345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701197721957398,"score_gpt":0.234819679686438,"score_spread":0.177807702466864,"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."}}