{"id":"W2766895107","doi":"10.1007/978-3-319-68600-4_40","title":"Model Derived Spike Time Dependent Plasticity","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Spike (software development); Learning rule; Spike-timing-dependent plasticity; Postsynaptic potential; Synaptic weight; Spiking neural network; Node (physics); Artificial neural network; Bidirectional associative memory; Artificial intelligence; Content-addressable memory","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":[0.0001571013,0.0004034061,0.0003835589,0.0002117197,0.0002670904,0.0001449596,0.001036432,0.0002111849,0.00002000999],"category_scores_gemma":[0.00005764442,0.000391539,0.00007464303,0.00004174833,0.0002883409,0.0002486582,0.0004086122,0.0007541564,0.00006760329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001702493,"about_ca_system_score_gemma":0.00008395217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001136356,"about_ca_topic_score_gemma":0.00001863225,"domain_scores_codex":[0.9982532,0.00000522871,0.0002611408,0.000632123,0.0003814511,0.0004668909],"domain_scores_gemma":[0.9990137,0.0001790133,0.00009961396,0.000529468,0.00005717945,0.0001210379],"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":[0.000003156743,0.000002528013,0.000001444934,0.00002600262,0.00000416729,0.00004581923,0.00006529736,0.9012266,0.009896168,0.0000746054,0.000003299388,0.08865087],"study_design_scores_gemma":[0.0001219209,0.00002721938,0.00001067175,0.0002297715,0.000007109481,0.00003076227,1.33262e-8,0.9530726,0.02338523,0.02262426,0.00006836275,0.0004220656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002980449,0.0001315109,0.9907219,0.00002003682,0.0007629017,0.000171821,0.000007225649,0.0002416596,0.004962438],"genre_scores_gemma":[0.915685,0.00003177621,0.08266776,0.000209033,0.0005530507,0.000003182519,0.000004061726,0.00007351159,0.0007726554],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9127045,"threshold_uncertainty_score":0.9998537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02403535354753704,"score_gpt":0.2392908017464042,"score_spread":0.2152554481988672,"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."}}