{"id":"W1966087949","doi":"10.5555/1639809.1655378","title":"Modeling of neural decoder based on binary spiking neurons in DEVS","year":2009,"lang":"en","type":"article","venue":"Spring Simulation Multiconference","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Decoding methods; Computer science; Binary number; Spike (software development); Spiking neural network; Spike sorting; Algorithm; SIGNAL (programming language); DEVS; Neural decoding; Artificial neural network; Artificial intelligence; Modeling and simulation; Simulation; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001532855,0.000227705,0.0003592466,0.0002031847,0.0002258186,0.0004854361,0.0008857744,0.0005457989,0.003007262],"category_scores_gemma":[0.00051095,0.000150734,0.0004148951,0.000152614,0.0003271645,0.0004220618,0.0003149198,0.0004028691,0.000259695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004915007,"about_ca_system_score_gemma":0.0005712735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002583182,"about_ca_topic_score_gemma":0.001814815,"domain_scores_codex":[0.9998825,0.00002135579,0.000008122053,0.00002160571,0.00004582381,0.00002064909],"domain_scores_gemma":[0.9998239,0.00006730011,0.00001956219,0.00002544853,0.00005207954,0.00001173111],"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":[0.00008721786,0.0000438543,0.001780061,0.0001109826,0.00002344041,0.0003033092,0.0001057161,0.9314341,0.02159555,0.03794187,0.0006562478,0.005917557],"study_design_scores_gemma":[0.000007052057,0.00001943032,0.0001575846,0.000004152929,0.000004136781,0.0000375822,0.000008772244,0.9905872,0.005768559,0.002277493,0.001124845,0.000003343231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5011388,0.0002489478,0.4570664,0.0002993828,0.0001372998,0.0001157619,0.001093414,0.001597647,0.03830238],"genre_scores_gemma":[0.9399714,0.0001766576,0.05053599,0.0000606508,0.00001240099,0.0001829711,0.0003582289,0.0001009666,0.008600613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003007262,"threshold_uncertainty_score":0.01006031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04017904174037024,"score_gpt":0.2896910768349047,"score_spread":0.2495120350945345,"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."}}