{"id":"W2900201430","doi":"10.1109/jqe.2018.2879484","title":"Numerical Implementation of Wavelength-Dependent Photonic Spike Timing Dependent Plasticity Based on VCSOA","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Quantum Electronics","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Ottawa; China Postdoctoral Science Foundation; University of Essex; State Key Laboratory of Integrated Services Networks; Xidian University; National Natural Science Foundation of China; University of Strathclyde","keywords":"Photonics; Spike-timing-dependent plasticity; Wavelength; Computer science; Photonic integrated circuit; Biasing; Neuromorphic engineering; Optoelectronics; Physics; Artificial neural network; Voltage; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002038275,0.0002623333,0.0004365649,0.0002421853,0.0003805537,0.0004203848,0.0008881298,0.0008261549,0.002150091],"category_scores_gemma":[0.0005572915,0.0002390278,0.0003964357,0.0002514667,0.000592427,0.0003937159,0.0004998159,0.0005494218,0.0001653266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005477588,"about_ca_system_score_gemma":0.0009059868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005852599,"about_ca_topic_score_gemma":0.005202735,"domain_scores_codex":[0.9999286,0.0000135389,0.000003656306,0.000009229002,0.00002790218,0.00001703464],"domain_scores_gemma":[0.9997993,0.00009884904,0.00002445626,0.00002101633,0.00003828718,0.00001816745],"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":[0.00003776526,0.00003758252,0.0004490706,0.00006785453,0.00002154087,0.0001112979,0.00003359722,0.960154,0.009771043,0.02407731,0.0002217628,0.005017112],"study_design_scores_gemma":[0.000006222129,0.000007390604,0.0000272403,0.000001612937,0.000001798455,0.000005193982,0.000002516301,0.99878,0.0004418985,0.0006045102,0.0001195474,0.000002094841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3889748,0.0006046389,0.5717812,0.0007347547,0.0002084445,0.0001429009,0.0002536159,0.0005969718,0.03670273],"genre_scores_gemma":[0.9239652,0.0001503439,0.07306232,0.00007311396,0.00001364642,0.0001807572,0.00004906467,0.00003438934,0.002471145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005852599,"threshold_uncertainty_score":0.01163709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307290587534765,"score_gpt":0.2959059902396732,"score_spread":0.2728330843643256,"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."}}