{"id":"W2517731408","doi":"10.1371/journal.pcbi.1005343","title":"Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface","year":2017,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Neurological Disorders and Stroke; University of Washington; Canadian Institutes of Health Research; Washington Research Foundation; National Institutes of Health; National Science Foundation","keywords":"Neuroscience; Computer science; Spike (software development); Spike-timing-dependent plasticity; Artificial neural network; Brain–computer interface; Stimulus (psychology); Motor cortex; Synaptic plasticity; Probabilistic logic; Biological neural network; Conditioning; Neuroplasticity; Artificial intelligence; Stimulation; Electroencephalography; Psychology; Chemistry; Mathematics","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":[0.0003955516,0.0005988391,0.0007073184,0.0004900717,0.0003401576,0.0005766752,0.001509541,0.001532133,0.002131893],"category_scores_gemma":[0.001078359,0.0004119898,0.0007923409,0.0004517615,0.001070885,0.0008894247,0.0006692108,0.0008129663,0.0003730486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009705238,"about_ca_system_score_gemma":0.000905092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008172895,"about_ca_topic_score_gemma":0.006525571,"domain_scores_codex":[0.9997807,0.00006539398,0.00001088279,0.00004434993,0.00005666663,0.0000421017],"domain_scores_gemma":[0.999643,0.0001480599,0.00007946198,0.00002411286,0.00006364706,0.00004170725],"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":[0.00004778665,0.00003524249,0.0003466648,0.00004588126,0.00002522862,0.0001813336,0.00003952233,0.9568626,0.005475155,0.03494061,0.0003564606,0.001643547],"study_design_scores_gemma":[0.000004167431,0.000006947311,0.00006714254,0.000001427312,0.000003611598,0.00001010404,0.000001653631,0.9984339,0.0001155442,0.00129624,0.00005636692,0.00000283629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2192333,0.0009357946,0.7432163,0.00113344,0.0001306621,0.0001142422,0.0004866935,0.000786625,0.03396291],"genre_scores_gemma":[0.9821108,0.0003116453,0.01146304,0.00007067174,0.00002534356,0.0001265366,0.00006909365,0.00005710153,0.005765838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008172895,"threshold_uncertainty_score":0.01625067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05314828997745757,"score_gpt":0.3005677178411256,"score_spread":0.247419427863668,"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."}}