{"id":"W4289938705","doi":"10.1016/j.neunet.2022.08.001","title":"Online spike sorting via deep contractive autoencoder","year":2022,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centrale des Syndicats du Québec; Group for Research in Decision Analysis","funders":"Australian Research Council","keywords":"Spike sorting; Spike (software development); Computer science; Autoencoder; Sorting; Artificial intelligence; Pattern recognition (psychology); Noise (video); Pipeline (software); Machine learning; Artificial neural network; Algorithm","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":[0.0001417595,0.0001859537,0.000190211,0.00006317515,0.0007089049,0.00005967515,0.0002839717,0.00004946871,0.0003675635],"category_scores_gemma":[0.0001221554,0.000176998,0.0001147555,0.0004182128,0.00006117651,0.0001994884,0.0002566932,0.0008298658,0.000008433375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008203947,"about_ca_system_score_gemma":0.00001193403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002621082,"about_ca_topic_score_gemma":0.00003171526,"domain_scores_codex":[0.9981104,0.0002274146,0.0002962629,0.0005245874,0.0003620383,0.0004793142],"domain_scores_gemma":[0.9991059,0.000333355,0.0002066683,0.0002299582,0.00002536725,0.00009870595],"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":[0.0002082491,0.0003052218,0.0006466993,0.000004448581,0.000007165308,0.0002186663,0.00008888497,0.8670606,0.04546048,0.0009439394,0.0006386902,0.08441696],"study_design_scores_gemma":[0.0003308023,0.0002524922,0.00146751,0.000002019313,0.0000107435,0.0001396966,0.0000395739,0.9947318,0.0003559006,0.0006566769,0.001812991,0.0001998011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672431,0.00009550057,0.02403507,0.001980097,0.004229843,0.00052019,0.00002953021,0.0003726978,0.001493936],"genre_scores_gemma":[0.9913934,0.00001061182,0.00008121621,0.007435962,0.0004241002,0.00003884662,0.00002805295,0.00003438298,0.0005534655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1276712,"threshold_uncertainty_score":0.721777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008111125922325,"score_gpt":0.2511521056987771,"score_spread":0.2310709944395539,"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."}}