{"id":"W4392816447","doi":"10.3389/fncom.2024.1348138","title":"The connectivity degree controls the difficulty in reservoir design of random boolean networks","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Reservoir computing; Excitatory postsynaptic potential; Computer science; Inhibitory postsynaptic potential; Degree (music); Balance (ability); Computation; Artificial neural network; Boolean data type; Value (mathematics); Recurrent neural network; Theoretical computer science; Algorithm; Neuroscience; Artificial intelligence; Physics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.002160416,0.0001816684,0.000256603,0.0001529711,0.0004198515,0.0005025191,0.001980923,0.0000520417,3.11246e-7],"category_scores_gemma":[0.0003669596,0.0001036873,0.00009589937,0.001872472,0.0004547105,0.0004273327,0.0004301654,0.000522531,7.306424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006370354,"about_ca_system_score_gemma":0.0001605932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000220609,"about_ca_topic_score_gemma":0.00001950889,"domain_scores_codex":[0.9971067,0.0007248322,0.0005003156,0.0005911466,0.0005864954,0.0004904685],"domain_scores_gemma":[0.9953302,0.004016429,0.0001278378,0.0003880052,0.00007518895,0.00006230304],"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":[0.00004538917,0.00003048684,0.001379245,0.0000067396,0.000003504266,0.00004212307,0.00009912108,0.9780767,0.00006192698,0.005292974,0.002586919,0.01237484],"study_design_scores_gemma":[0.0004973531,0.00006051828,0.02316179,0.00007598016,0.000001969062,0.00001787183,0.00002118716,0.9602522,0.00001116257,0.01518627,0.0005971831,0.0001165192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718607,0.001982912,0.97194,0.005150563,0.00308775,0.0005401232,0.000001721633,0.00006485031,0.00004604811],"genre_scores_gemma":[0.9893179,0.0001020049,0.01007316,0.000340362,0.00008529939,0.00002805697,6.621365e-7,0.000009363006,0.00004320628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9721318,"threshold_uncertainty_score":0.4845803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03033780363879173,"score_gpt":0.2584488936362918,"score_spread":0.2281110899975001,"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."}}