{"id":"W4297619716","doi":"10.1145/3546790.3546814","title":"Think Fast: Time Control in Varying Paradigms of Spiking Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"U.S. Department of Energy; National Science Foundation","keywords":"Spiking neural network; Computer science; Artificial intelligence; Deep learning; Artificial neural network; Machine learning; Construct (python library); Inference; Process (computing); Task (project management); Engineering","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":[0.0001308952,0.00009664906,0.0001775162,0.000060704,0.00007168483,0.000006019878,0.0001245669,0.00001947121,0.0001459576],"category_scores_gemma":[0.000006301908,0.0001011921,0.00004291657,0.0001997878,0.000009384877,0.00009478845,0.00005728016,0.0003100512,0.000001833484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003467969,"about_ca_system_score_gemma":0.000002599602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003002557,"about_ca_topic_score_gemma":7.049294e-7,"domain_scores_codex":[0.9993132,0.00003519872,0.0002169725,0.0001129025,0.00009243964,0.0002292325],"domain_scores_gemma":[0.9997087,0.0001206797,0.00003026922,0.0001074898,0.000004676069,0.00002816878],"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":[0.00001329514,0.000006468108,0.0002028822,0.000008487735,0.000005744485,0.00001935378,0.0001518346,0.9845593,0.009135316,0.0001245266,0.00002328187,0.005749492],"study_design_scores_gemma":[0.0004064155,0.00003402181,0.0002072424,0.000007619417,0.000003478032,0.0000149825,0.00003950403,0.9980162,0.0009751604,0.0001001548,0.00008779491,0.0001073837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9303551,0.000486291,0.06267782,0.00003905546,0.0004031642,0.000207443,0.000002429335,0.0003350931,0.005493555],"genre_scores_gemma":[0.9994862,0.000002690098,0.0002308374,0.0001446572,0.00005490044,0.000004884804,0.00000217743,0.00001866427,0.00005497754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06913105,"threshold_uncertainty_score":0.4126495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007494890112077407,"score_gpt":0.1989567625280966,"score_spread":0.1914618724160192,"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."}}