{"id":"W4294237697","doi":"10.1038/s41467-022-32646-w","title":"Small, correlated changes in synaptic connectivity may facilitate rapid motor learning","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Research Councils UK; Wellcome Trust; Simons Foundation","keywords":"Covariance; Neuroscience; Adaptation (eye); Inhibitory postsynaptic potential; Nerve net; Computer science; Local adaptation; Motor learning; Artificial neural network; Biology; Artificial intelligence; Mathematics; Statistics; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002643134,0.0002954209,0.0002463188,0.0001227719,0.0001266498,0.0003483563,0.0003596723,0.0002380559,0.002374538],"category_scores_gemma":[0.001121084,0.0002064798,0.0002286139,0.00007830501,0.0004633953,0.0005611971,0.000641734,0.0003595313,0.0002137161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002274281,"about_ca_system_score_gemma":0.0002265728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004023651,"about_ca_topic_score_gemma":0.0006694745,"domain_scores_codex":[0.9999074,0.00001576646,0.000007557861,0.00002844951,0.00001723604,0.00002361536],"domain_scores_gemma":[0.9996208,0.00009710856,0.0000864059,0.00007763093,0.00003656542,0.00008150376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001511915,0.00009520293,0.002425654,0.00006480455,0.00003821281,0.0001581102,0.00003714477,0.0156697,0.9682445,0.002209364,0.0001695886,0.01073655],"study_design_scores_gemma":[0.0001149954,0.00156859,0.1365665,0.00003733837,0.0001084729,0.0004637319,0.0001427344,0.4145656,0.4298612,0.01421033,0.002290216,0.00007025785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733054,0.00006379817,0.02414745,0.00007007673,0.00001861735,0.00002130898,0.0000662286,0.0002787196,0.0020284],"genre_scores_gemma":[0.9976606,0.00001867687,0.002029985,0.00001186692,0.000002639321,0.000007513673,0.00002172462,0.00001619405,0.0002308633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002374538,"threshold_uncertainty_score":0.007943571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641229947724604,"score_gpt":0.2765663771999761,"score_spread":0.2124433824275156,"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."}}