{"id":"W2518386427","doi":"10.1109/iscas.2016.7539100","title":"FPGA minimal components SKAN model for classical and operant conditioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Field-programmable gate array; Operant conditioning; Computer science; Process (computing); Kernel (algebra); Architecture; Spiking neural network; Artificial neural network; Computer architecture; Conditioning; Artificial intelligence; Embedded system; Engineering; 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":[0.00007802802,0.000397258,0.0002617163,0.0001544009,0.0001893455,0.0003551267,0.0009548036,0.0003026281,0.004281425],"category_scores_gemma":[0.0001273686,0.0001056223,0.0003537196,0.0001336079,0.0001914945,0.0004069828,0.0001781911,0.000560489,0.0007001544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005081216,"about_ca_system_score_gemma":0.0003997293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002359777,"about_ca_topic_score_gemma":0.003935744,"domain_scores_codex":[0.9999363,0.00001239908,0.000003500804,0.00001205003,0.0000248367,0.0000109795],"domain_scores_gemma":[0.9999484,0.00001157727,0.000005344184,0.00001361874,0.00001572334,0.000005267716],"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":[0.0002976373,0.00008834051,0.0006942942,0.0002040612,0.00007291962,0.0003004513,0.0000635032,0.8080217,0.05001519,0.08756921,0.002724988,0.04994777],"study_design_scores_gemma":[0.00001430676,0.00005938913,0.0001599305,0.000004418983,0.00001825869,0.00005122242,0.000004303111,0.9877752,0.004442161,0.004468726,0.002996115,0.000005978541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09694713,0.0005416871,0.8689075,0.0002456331,0.0001792375,0.00009918171,0.0002685087,0.001373786,0.0314374],"genre_scores_gemma":[0.8997445,0.0002921108,0.0880907,0.00007228573,0.0000213915,0.0001225281,0.0001589107,0.00007033769,0.01142718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004281425,"threshold_uncertainty_score":0.01432282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745321956766039,"score_gpt":0.2544908667870374,"score_spread":0.217037647219377,"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."}}