{"id":"W2767003055","doi":"","title":"A General Purpose Architecture for Building Spiking Neuron Models of Biological Cognition - eScholarship","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Meeting of the Cognitive Science Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Cognitive architecture; Object (grammar); Phrase; Set (abstract data type); Artificial intelligence; Cognition; Cognitive model; Cognitive science; Psychology; Programming language; Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088443,0.0001679926,0.00023255,0.00004166978,0.0003663211,0.00003518751,0.0007252057,0.0000692789,0.000001012376],"category_scores_gemma":[0.001271565,0.0001052099,0.000290824,0.0006414088,0.0007969895,0.000623451,0.0003877664,0.00031124,1.716946e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002673068,"about_ca_system_score_gemma":0.000023629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003332733,"about_ca_topic_score_gemma":1.145407e-7,"domain_scores_codex":[0.9986876,0.00001481583,0.0003349058,0.0002655963,0.0003390905,0.0003579661],"domain_scores_gemma":[0.998289,0.0002474691,0.0003272872,0.00007862142,0.001009406,0.00004814548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001433801,0.00001631384,0.0004202911,0.0001768467,0.00001400654,1.134736e-8,0.001450532,0.006604132,0.9880143,0.0003998275,0.00001363801,0.002875766],"study_design_scores_gemma":[0.0002437762,0.00008440918,0.002001464,0.000650811,0.00003544063,0.000004633282,0.002035024,0.01983721,0.953826,0.02114172,0.000004108055,0.0001354449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969227,0.00005845283,0.001562612,0.00007549998,0.0001679454,0.0006781691,0.00002630283,0.00004911016,0.0004592582],"genre_scores_gemma":[0.9921505,0.000009871914,0.007629307,0.00008905503,0.00007227665,0.00002541394,3.64942e-7,0.00001598327,0.000007192377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03418834,"threshold_uncertainty_score":0.4290337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02897060855535204,"score_gpt":0.261498610510597,"score_spread":0.232528001955245,"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."}}