{"id":"W2296127811","doi":"10.1016/j.neucom.2016.01.003","title":"Effect of spike-timing-dependent plasticity on neural assembly computing","year":2016,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Spiking neural network; Spike-timing-dependent plasticity; Memorization; Artificial intelligence; Spike (software development); Neural ensemble; Convergence (economics); Similarity (geometry); Pattern recognition (psychology); Synaptic plasticity; Machine learning; Mathematics; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000319178,0.000365115,0.0004495743,0.000138748,0.0001598891,0.00002381723,0.0002988115,0.00008057931,0.000007742638],"category_scores_gemma":[0.0002489415,0.0002680773,0.0001385666,0.0002029227,0.00004461138,0.0001291238,0.0001565518,0.0003455686,0.00003114952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005650791,"about_ca_system_score_gemma":0.000006420703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001120852,"about_ca_topic_score_gemma":3.469799e-7,"domain_scores_codex":[0.9980378,0.0001560613,0.000500124,0.0004202134,0.0003100084,0.0005758585],"domain_scores_gemma":[0.9969558,0.002504036,0.0001494097,0.0002314009,0.00003665492,0.0001227722],"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":[0.00006997633,0.00001823886,0.0008807064,0.0001905709,0.00001632934,0.00004987587,0.00004754066,0.3717313,0.4104086,0.00006277963,0.00003174263,0.2164923],"study_design_scores_gemma":[0.001030213,0.0007646583,0.001985599,0.0003824452,0.00002047641,0.00005005962,0.000003864386,0.2407685,0.7545939,0.00001446462,0.00006439781,0.0003214226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9545529,0.00002071449,0.04263148,0.00002321823,0.0008314741,0.0002437887,0.000002910239,0.000671209,0.00102225],"genre_scores_gemma":[0.999127,0.000002482029,0.0003490584,0.00006150556,0.0003724295,0.000002022096,6.435343e-7,0.00006883715,0.00001605771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3441853,"threshold_uncertainty_score":0.9999772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376065924750072,"score_gpt":0.2556931063017496,"score_spread":0.2419324470542489,"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."}}