{"id":"W4283583520","doi":"10.1073/pnas.2115229119","title":"Learning in deep neural networks and brains with similarity-weighted interleaved learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"National Institute of Neurological Disorders and Stroke; Defense Advanced Research Projects Agency; National Institutes of Health","keywords":"Interleaving; Computer science; MNIST database; Artificial intelligence; Similarity (geometry); Artificial neural network; Speedup; Deep learning; ENCODE; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169326,0.0006759514,0.0006065952,0.0006278666,0.000441413,0.001519045,0.001565833,0.001469623,0.001848174],"category_scores_gemma":[0.004390556,0.0004974387,0.000666199,0.0008518785,0.002783526,0.004039117,0.001978199,0.002427499,0.0003614355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173339,"about_ca_system_score_gemma":0.0007124477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352324,"about_ca_topic_score_gemma":0.002227185,"domain_scores_codex":[0.9995338,0.0001830964,0.00002402942,0.00010608,0.00009543867,0.00005755488],"domain_scores_gemma":[0.9989148,0.0005753707,0.00014055,0.0001910824,0.0001151633,0.00006307278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009300227,0.00006769196,0.0008758508,0.0001751995,0.00007224088,0.0001275708,0.0001759135,0.376455,0.003391498,0.5640185,0.002160866,0.05238676],"study_design_scores_gemma":[0.000009724789,0.0000270456,0.000105499,0.00001689851,0.000006103834,0.00002639527,0.00001145563,0.6656408,0.0007997212,0.3323053,0.001042119,0.000008889041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03217588,0.002077371,0.9558218,0.002406023,0.0001404833,0.000039755,0.0000773386,0.0004346313,0.006826713],"genre_scores_gemma":[0.6999664,0.002685817,0.2890938,0.0007582539,0.0003017886,0.000276314,0.00017619,0.0001317438,0.006609674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002352324,"threshold_uncertainty_score":0.008513153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02795460706584814,"score_gpt":0.2628016375285744,"score_spread":0.2348470304627263,"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."}}