{"id":"W2004963987","doi":"10.1007/s11571-009-9083-3","title":"Are binary synapses superior to graded weight representations in stochastic attractor networks?","year":2009,"lang":"en","type":"article","venue":"Cognitive Neurodynamics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Attractor; Binary number; Representation (politics); Mathematics; Synaptic weight; Noise (video); Applied mathematics; Statistical physics; Computer science; Artificial neural network; Mathematical analysis; Physics; Artificial intelligence; Arithmetic","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":[0.00003903015,0.0002115843,0.0002218417,0.0001698431,0.00008859942,0.00002972205,0.0001202062,0.00005888289,0.000007788417],"category_scores_gemma":[0.0002896157,0.0002369935,0.00005343604,0.0005199757,0.00002813372,0.0002078286,0.0000349516,0.0003652573,0.00001752095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004644434,"about_ca_system_score_gemma":0.00000657493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.321915e-7,"about_ca_topic_score_gemma":0.00001500429,"domain_scores_codex":[0.9989382,0.00004267321,0.0002439929,0.0003193715,0.0001004622,0.0003553159],"domain_scores_gemma":[0.9993158,0.0003202507,0.00004448113,0.0001468009,0.00005375594,0.0001188862],"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":[0.00005829344,0.00006192549,0.001244027,0.00001323175,0.000008622567,0.0003163861,0.0001613349,0.9820228,0.01159836,0.00004609198,0.00003163665,0.004437312],"study_design_scores_gemma":[0.000760869,0.0002127821,0.4852233,0.0003266465,0.00003432453,0.00005673451,0.0003126028,0.5108038,0.001367341,0.0003041438,0.00001888,0.0005785211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551524,0.0001100762,0.04345103,0.0001012391,0.0004113763,0.0003470984,0.00001797263,0.0002810467,0.0001277954],"genre_scores_gemma":[0.999112,0.00002471727,0.0001006931,0.0005306087,0.0001279542,0.00001778739,0.00002320186,0.00003578323,0.00002723588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4839793,"threshold_uncertainty_score":0.9664314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995941892030887,"score_gpt":0.2681281649636706,"score_spread":0.2481687460433618,"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."}}