{"id":"W4407766626","doi":"10.1063/5.0233158","title":"Role of short-term plasticity and slow temporal dynamics in enhancing time series prediction with a brain-inspired recurrent neural network","year":2025,"lang":"en","type":"article","venue":"Chaos An Interdisciplinary Journal of Nonlinear Science","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Random graph; Population; Recurrent neural network; Network dynamics; Artificial intelligence; Robustness (evolution); Artificial neural network; Log-normal distribution; Machine learning; Graph; Neuroscience; Theoretical computer science; Mathematics; Psychology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000618445,0.0001757123,0.0003217753,0.000394998,0.0003408529,0.0001148125,0.0004084209,0.00005044044,0.000004840588],"category_scores_gemma":[0.0001542955,0.0001342284,0.00005601702,0.0009525575,0.0006676003,0.001284677,0.0004029532,0.0003506412,3.958153e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001757853,"about_ca_system_score_gemma":0.0002011966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003338783,"about_ca_topic_score_gemma":0.0001931379,"domain_scores_codex":[0.998206,0.00008760818,0.0006104435,0.0003811878,0.0004059775,0.0003087466],"domain_scores_gemma":[0.9990844,0.0001462323,0.0003150634,0.0001560325,0.0001692745,0.0001289879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002230663,0.0005555957,0.06155592,0.0001131737,0.0000143948,0.0001202334,0.001601895,0.01202621,0.901314,0.0006329429,0.0000387134,0.0197963],"study_design_scores_gemma":[0.0004661032,0.002907525,0.05826258,0.0006944258,0.00001771594,0.0004253054,0.0006318912,0.9216201,0.01409093,0.0007096856,0.000009267442,0.000164432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976377,0.00002471731,0.000980294,0.0005511007,0.0004636603,0.0001708158,0.0000260713,0.0000154457,0.0001302119],"genre_scores_gemma":[0.998786,0.00001354084,0.0009565465,0.00004897535,0.0001227512,0.000002408729,0.000003291262,0.000009644559,0.00005680647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9095939,"threshold_uncertainty_score":0.5473673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075901035696258,"score_gpt":0.2752426498486397,"score_spread":0.2644836394916771,"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."}}