{"id":"W820619723","doi":"10.1007/978-1-4613-0277-3_28","title":"Dynamics of Neural Networks with Delay: Attractors and Content-Addressable Memory","year":2001,"lang":"en","type":"book-chapter","venue":"Differential Equations and Nonlinear Mechanics","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Artificial neural network; Attractor; Computer science; Content-addressable memory; Computation; Cognition; Cognitive science; Artificial intelligence; Nervous system network models; Network dynamics; Dynamics (music); Recurrent neural network; Neuroscience; Psychology; Types of artificial neural networks; Algorithm; Mathematics","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":[0.0001116646,0.0003217705,0.0004431902,0.000114655,0.0001838079,0.0001263248,0.0002560393,0.0002907444,0.00005307213],"category_scores_gemma":[0.00001646037,0.0002740983,0.00007425639,0.00009018209,0.00008743517,0.0002441969,0.0002172735,0.0003624143,7.46316e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003922907,"about_ca_system_score_gemma":0.00004571223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003489524,"about_ca_topic_score_gemma":0.0004964065,"domain_scores_codex":[0.9985372,0.00003843318,0.000424179,0.000476643,0.0002876649,0.0002358106],"domain_scores_gemma":[0.9987398,0.0001734415,0.0003259699,0.0004126338,0.0002258928,0.0001222248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006777244,0.0001095928,0.00003644442,0.000111217,0.0001470829,0.00002015659,0.0001200442,0.006843338,0.00002950514,0.9236721,0.00001803418,0.06882474],"study_design_scores_gemma":[0.0003939528,0.0002441788,0.00001197635,0.00008784581,0.00009363887,0.00002160898,0.00001575111,0.9938673,0.00001022355,0.004842173,0.0001278563,0.0002834841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002603194,0.0005509874,0.9948581,0.0001647295,0.0003047402,0.0003655687,0.00007083711,0.00005972628,0.001022105],"genre_scores_gemma":[0.9686686,0.002868878,0.009089829,0.0001747738,0.0005269614,0.00002049437,0.001274022,0.0001362814,0.01724019],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9870239,"threshold_uncertainty_score":0.9999711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03019759471547651,"score_gpt":0.2199412659058758,"score_spread":0.1897436711903993,"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."}}