{"id":"W4389475089","doi":"10.1038/s41467-023-43887-8","title":"Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Neuromorphic engineering; Computer science; Network topology; Hebbian theory; Leverage (statistics); Artificial neural network; Structural plasticity; Software; Artificial intelligence; Synaptic plasticity; MNIST database; Neuroscience; Computer network; Biology","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":[0.00008958096,0.0001565554,0.0001147502,0.0001209376,0.0001382802,0.0002672343,0.0002450849,0.0002469222,0.001137053],"category_scores_gemma":[0.0003839968,0.00009639869,0.0001131762,0.0000962486,0.0002267954,0.0004039542,0.0002379765,0.0003145102,0.0002318531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002121253,"about_ca_system_score_gemma":0.0001174974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002172165,"about_ca_topic_score_gemma":0.0004753753,"domain_scores_codex":[0.9999616,0.000004954152,0.000002262688,0.000008217691,0.00001474139,0.000008217126],"domain_scores_gemma":[0.9998939,0.00004072965,0.00001885785,0.0000189889,0.00001301545,0.00001459764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001078415,0.00008013828,0.0005465683,0.0001980357,0.00001628645,0.000357085,0.00005944114,0.05599937,0.8953708,0.01726987,0.0008223975,0.02917213],"study_design_scores_gemma":[0.00002994858,0.0002961637,0.001990209,0.00002800842,0.00001707193,0.0003954828,0.00004975848,0.4564165,0.5236208,0.008799462,0.008336295,0.00002036434],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8610206,0.0006864434,0.1283796,0.0003499673,0.0001290685,0.0000422345,0.0001754104,0.0007078492,0.008509008],"genre_scores_gemma":[0.985023,0.0001862694,0.01370105,0.00003333997,0.000005793519,0.00001876132,0.00004243914,0.00002745138,0.0009618859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001137053,"threshold_uncertainty_score":0.00380379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02955815720924175,"score_gpt":0.2765491756677649,"score_spread":0.2469910184585232,"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."}}