{"id":"W4285218612","doi":"10.1109/tcsii.2022.3187623","title":"A High-Accuracy Digital Implementation of the Morris–Lecar Neuron With Variable Physiological Parameters","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"CORDIC; Field-programmable gate array; Computer science; Acceleration; Rotation (mathematics); Computer hardware; Variable (mathematics); State (computer science); Artificial intelligence; Algorithm; 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":[0.0002235679,0.0003612567,0.0003214552,0.0002488958,0.0002238289,0.0006422685,0.001146574,0.0004536151,0.003839769],"category_scores_gemma":[0.000612081,0.0001540361,0.0002238746,0.0002759916,0.0002387608,0.0004628415,0.0003750381,0.0005258878,0.00134697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004537654,"about_ca_system_score_gemma":0.0006917663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003025432,"about_ca_topic_score_gemma":0.004635345,"domain_scores_codex":[0.99986,0.00001410116,0.000009575124,0.00002950083,0.00007357053,0.00001323947],"domain_scores_gemma":[0.9998336,0.00003433049,0.0000163124,0.00004253342,0.0000621595,0.00001100817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003815757,0.0001599915,0.002591427,0.0006002004,0.0001483157,0.0006680737,0.0002381418,0.2396637,0.2629028,0.05033563,0.007885285,0.4344248],"study_design_scores_gemma":[0.00005948559,0.0002679688,0.001018515,0.00004447199,0.00005284535,0.0006186576,0.00002788177,0.9033248,0.0667332,0.004733313,0.02306742,0.00005150278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01570882,0.0003009666,0.9743376,0.0001619493,0.0001389604,0.00006686503,0.0001382585,0.002482906,0.006663755],"genre_scores_gemma":[0.4981699,0.0003749455,0.4936272,0.0001191976,0.00004330197,0.0001535978,0.0002471145,0.0001334841,0.007131388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003839769,"threshold_uncertainty_score":0.01284528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180889104765702,"score_gpt":0.2238597940683428,"score_spread":0.2057708835917726,"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."}}