{"id":"W2527798464","doi":"10.3389/fncom.2017.00024","title":"Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":460,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Concordia University; Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Samsung; Université de Montréal; Compute Canada; Canadian Institute for Advanced Research","keywords":"Backpropagation; Computer science; Artificial neural network; Propagation of uncertainty; Algorithm; Computation; Hebbian theory; Error function; Artificial intelligence","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":[0.001950454,0.001576739,0.001495728,0.001014862,0.0005761214,0.002458981,0.003864237,0.003065456,0.003727709],"category_scores_gemma":[0.007537158,0.0009656119,0.001175317,0.0009278163,0.00275172,0.006825112,0.003520288,0.004511261,0.001009246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613136,"about_ca_system_score_gemma":0.001571784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004056208,"about_ca_topic_score_gemma":0.002773251,"domain_scores_codex":[0.9990463,0.0002715717,0.00005368535,0.0001749886,0.0003610095,0.00009252469],"domain_scores_gemma":[0.9971893,0.001790851,0.0001931736,0.0003625295,0.000334053,0.0001300426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007237837,0.00005682778,0.0003525108,0.0001653515,0.00008563432,0.0001063,0.0001478672,0.3864259,0.001489885,0.5598796,0.0017719,0.0494458],"study_design_scores_gemma":[0.000009323543,0.00002278977,0.00003637287,0.00002358268,0.000008636142,0.00002706673,0.000006416172,0.7657437,0.0004940661,0.231278,0.002334566,0.00001540831],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003099721,0.0008467734,0.9918937,0.0006776488,0.000107707,0.00001892972,0.00002885438,0.0002342555,0.003092283],"genre_scores_gemma":[0.5101894,0.005464619,0.4664384,0.001430859,0.0008273229,0.0003289602,0.0002271394,0.0009158879,0.01417739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004056208,"threshold_uncertainty_score":0.01247042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04478735180542688,"score_gpt":0.2632446253063672,"score_spread":0.2184572735009403,"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."}}