{"id":"W2948151899","doi":"10.1101/662072","title":"Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données","keywords":"Neurostimulation; Computer science; Bayesian optimization; Process (computing); Artificial intelligence; Computation; Machine learning; Algorithm; Neuroscience; Stimulation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001087796,0.0006374333,0.0006001922,0.0004119145,0.0002658369,0.0004719752,0.0009161448,0.001341087,0.001655362],"category_scores_gemma":[0.002834426,0.0004584767,0.0005189469,0.0003812476,0.001014644,0.0006641957,0.001088551,0.0009331875,0.0002836599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008128979,"about_ca_system_score_gemma":0.001062206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003950629,"about_ca_topic_score_gemma":0.004668543,"domain_scores_codex":[0.9997241,0.00008962006,0.00001852788,0.00006887088,0.00006724553,0.00003173825],"domain_scores_gemma":[0.9991406,0.000578227,0.00008655767,0.00004653463,0.0001030004,0.00004494717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005320153,0.00004662784,0.0003918302,0.0000376637,0.00002772812,0.0000258602,0.0000532626,0.9523572,0.004673752,0.005709066,0.0004343021,0.03618959],"study_design_scores_gemma":[0.000006205579,0.00001252886,0.00003216339,0.000001569302,0.000001325758,0.000003762758,0.000001340536,0.9986238,0.0003126605,0.0009395195,0.00006318015,0.000001919876],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01495965,0.00005130617,0.9836737,0.0001234553,0.000009269792,0.00004614062,0.00001141176,0.0002876497,0.0008374799],"genre_scores_gemma":[0.4976783,0.00007152101,0.4988199,0.0001922453,0.00002205139,0.0003562513,0.00005212572,0.0001192464,0.002688367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003950629,"threshold_uncertainty_score":0.007855237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03445168050843529,"score_gpt":0.2503874131333143,"score_spread":0.215935732624879,"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."}}