{"id":"W2340581119","doi":"10.1109/lascas.2016.7451011","title":"A modified synapse model for neuromorphic circuits","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Neuromorphic engineering; Computer science; Implementation; Biological neural network; Synapse; Biological neuron model; Transmission (telecommunications); Computer architecture; Behavioral modeling; Artificial neural network; Neuroscience; Artificial intelligence; Machine learning; Telecommunications","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":[0.00003357113,0.00008194779,0.00008220737,0.00002409215,0.00003493852,0.000005147396,0.00007373184,0.00002804632,0.000008925108],"category_scores_gemma":[0.00002679712,0.00005615798,0.00003629276,0.00003543356,0.000009132435,0.00009896528,0.00001237462,0.00003462485,0.00001350973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001256442,"about_ca_system_score_gemma":0.000003813522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.272546e-8,"about_ca_topic_score_gemma":5.696468e-7,"domain_scores_codex":[0.9995627,0.000003255977,0.00009603192,0.0001144653,0.00003826697,0.0001853048],"domain_scores_gemma":[0.9997178,0.00009489694,0.000008762355,0.0001165813,0.00001491104,0.00004702021],"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":[0.000005440044,0.00000437269,0.000003692653,0.00003302753,0.000006818498,0.000002914335,0.00002349848,0.5921457,0.3891081,0.003862934,0.0003603528,0.01444315],"study_design_scores_gemma":[0.0003295944,0.00001632547,0.00001125506,0.0000164434,0.000003465397,0.000006074364,9.614491e-7,0.9360618,0.06032637,0.003008103,0.0001059098,0.0001137103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2951422,0.00001501845,0.7032135,0.00007302086,0.00008676777,0.00009973506,0.00000449612,0.0003621122,0.00100311],"genre_scores_gemma":[0.9979198,0.000007460369,0.0007102453,0.00008876015,0.00004652334,0.00001695221,4.867612e-7,0.00002246454,0.001187323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7027776,"threshold_uncertainty_score":0.2290056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06964378572002056,"score_gpt":0.2405587493544494,"score_spread":0.1709149636344289,"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."}}