{"id":"W3103190104","doi":"10.1101/2020.11.10.350876","title":"Learning function from structure in neuromorphic networks","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canada Research Chairs; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; McGill University; Canadian Institute for Advanced Research","keywords":"Neuromorphic engineering; Connectome; Computer science; Artificial intelligence; Connectomics; Reservoir computing; Modular design; Artificial neural network; Network dynamics; Nervous system network models; Computational neuroscience; Machine learning; Neuroscience; Recurrent neural network; Functional connectivity; Types of artificial neural networks; 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":[0.0003362746,0.0002442903,0.0002486814,0.0003116704,0.0001950851,0.0005505387,0.0003262597,0.0005289216,0.001479465],"category_scores_gemma":[0.002468197,0.0001679526,0.0001689078,0.0001918752,0.0007276838,0.0007963143,0.000584113,0.0004366604,0.0001341755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004272152,"about_ca_system_score_gemma":0.0002810468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009267384,"about_ca_topic_score_gemma":0.0008031214,"domain_scores_codex":[0.999925,0.00002979111,0.000003246211,0.00001493422,0.00001508045,0.00001194591],"domain_scores_gemma":[0.9995033,0.0002987444,0.0000688577,0.00004594302,0.00004635151,0.00003689031],"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":[0.00004920744,0.0000375131,0.001797059,0.00006478407,0.00003091859,0.00009325027,0.000078668,0.9007324,0.02065399,0.05815193,0.0007046453,0.01760576],"study_design_scores_gemma":[0.000003660388,0.00001003023,0.0003164625,0.000004175226,0.000001875243,0.00001677934,0.000008041216,0.9717277,0.001202883,0.02650968,0.0001958743,0.000002930174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6359057,0.0003802127,0.3554639,0.001374279,0.0000459913,0.00003314715,0.0001005541,0.0004419148,0.006254253],"genre_scores_gemma":[0.9854813,0.00009888816,0.01339302,0.00003428229,0.00000929652,0.00002003035,0.00002702119,0.00002049719,0.0009156268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001479465,"threshold_uncertainty_score":0.004949331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02686065065982299,"score_gpt":0.2033680307526922,"score_spread":0.1765073800928692,"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."}}