{"id":"W4285743412","doi":"10.3389/fnins.2022.908330","title":"Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Deutsche Forschungsgemeinschaft; Nvidia","keywords":"Noise (video); Auditory cortex; Computer science; Speech recognition; Context (archaeology); Hearing loss; Auditory system; Neuroscience; Psychology; Artificial intelligence; Audiology; Biology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001510193,0.00005249571,0.00007098949,0.00009609377,0.00008676521,0.000009799997,0.0002134088,0.000007783209,0.000003490343],"category_scores_gemma":[0.00001999639,0.00004737743,0.00002916247,0.0003997711,0.00007795438,0.00008012381,0.0001114585,0.0001437925,2.228486e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004342625,"about_ca_system_score_gemma":0.0001385983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003035624,"about_ca_topic_score_gemma":0.00000147477,"domain_scores_codex":[0.9993294,0.00003802725,0.0001513187,0.0001746638,0.0001991665,0.0001074224],"domain_scores_gemma":[0.9997531,0.00001635321,0.00009494329,0.00009511607,0.00002622683,0.00001422289],"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":[0.00003842076,0.0004459046,0.06203599,0.00001129898,0.000002035482,9.095853e-7,0.0006413893,0.7938718,0.02277946,0.002945821,0.001518985,0.115708],"study_design_scores_gemma":[0.0002113425,0.00002204929,0.03024733,0.000005569869,0.000001336821,2.999178e-7,0.0001730648,0.9296342,0.0001946418,0.03941087,0.00004184707,0.00005748989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8885576,0.000004519396,0.1084798,0.0001170025,0.002153048,0.0002136189,0.00008265477,0.000004418718,0.0003872837],"genre_scores_gemma":[0.9966301,2.438659e-7,0.003147435,0.00007831585,0.0000349492,0.00003464365,0.000007207202,0.000003887664,0.00006318585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1357624,"threshold_uncertainty_score":0.1931996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01101494828131457,"score_gpt":0.2048670803679518,"score_spread":0.1938521320866373,"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."}}