{"id":"W4410009330","doi":"10.1088/2634-4386/add293","title":"Enhancing temporal learning in recurrent spiking networks for neuromorphic applications","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada","keywords":"Neuromorphic engineering; Spiking neural network; Computer science; Artificial intelligence; Neuroscience; Computer architecture; Artificial neural network; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002879196,0.0002779778,0.0003218345,0.000268725,0.0002065741,0.00006550732,0.0001440308,0.00008697598,9.460865e-7],"category_scores_gemma":[0.00008675069,0.0003396746,0.00006192174,0.0005472577,0.00002002674,0.00008798503,0.00009575437,0.0007124882,6.931328e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000487442,"about_ca_system_score_gemma":0.00001302776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002761322,"about_ca_topic_score_gemma":0.000006128387,"domain_scores_codex":[0.998571,0.0000262716,0.0004392097,0.0003859376,0.00007806352,0.0004995344],"domain_scores_gemma":[0.9991575,0.0005273869,0.00004835811,0.0001595139,0.00002849617,0.00007867737],"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":[0.000006003163,0.000009508421,0.0009076134,0.0003442473,0.00000967164,0.0000095347,0.00005574222,0.9645646,0.01072082,0.0003551532,0.00001025435,0.02300688],"study_design_scores_gemma":[0.0004240655,0.0000445287,0.002377426,0.0004429844,0.00001391703,0.00002420064,0.0000207469,0.9909959,0.00164741,0.00005878413,0.003665643,0.0002843771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4269181,0.0009904832,0.5703444,0.00004516241,0.0005952275,0.0003461491,9.145151e-7,0.0006964562,0.00006312339],"genre_scores_gemma":[0.9970745,0.00008999207,0.002443121,0.00005059626,0.0002199929,0.00003703268,0.00001051346,0.00005201967,0.00002219961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5701565,"threshold_uncertainty_score":0.9999055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616495371023238,"score_gpt":0.2340718473495923,"score_spread":0.2179068936393599,"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."}}