{"id":"W2555477762","doi":"10.1109/ijcnn.2016.7727892","title":"Robotic implementation of classical and Operant Conditioning as a single STDP learning process","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Operant conditioning; Computer science; Spiking neural network; Adaptation (eye); Spike-timing-dependent plasticity; Process (computing); Conditioning; Field-programmable gate array; Artificial intelligence; Artificial neural network; Robot; Kernel (algebra); Classical conditioning; Neuroscience; Embedded system; Synaptic plasticity; Engineering; Psychology","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":[0.00003615658,0.0000555792,0.00007941498,0.00002508294,0.00004409063,0.000007956415,0.0000226775,0.00001571992,0.0000854879],"category_scores_gemma":[0.00001771557,0.00003981453,0.00001072427,0.00004432873,0.00001910308,0.0001627207,0.00001111414,0.00004580097,0.000003934937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001348573,"about_ca_system_score_gemma":0.000004718431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001184463,"about_ca_topic_score_gemma":0.000002986657,"domain_scores_codex":[0.9996387,0.000009977351,0.0001171238,0.00007947395,0.00005281306,0.0001019536],"domain_scores_gemma":[0.9998397,0.00006424417,0.00001975593,0.0000292377,0.0000186845,0.00002839115],"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":[0.000005417354,0.000007078517,0.001315539,0.00006735838,0.00001172525,0.000002968213,0.0003500563,0.05087696,0.903374,0.00078582,0.000008268459,0.0431948],"study_design_scores_gemma":[0.000661733,0.0002202909,0.001415177,0.0001119748,0.00001051069,0.00002377658,0.001042968,0.01022444,0.9855063,0.0005625681,0.00007677516,0.0001435191],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9400299,0.00002563389,0.05914288,0.00004804169,0.00003307524,0.00005565503,3.601118e-7,0.0001011584,0.0005632724],"genre_scores_gemma":[0.9994653,0.000005903232,0.0003803982,0.00001214809,0.00002161023,0.000002815056,0.000001175392,0.000009230007,0.0001014206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08213225,"threshold_uncertainty_score":0.1623589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452184771434572,"score_gpt":0.2833774829422313,"score_spread":0.2688556352278855,"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."}}