{"id":"W4220665114","doi":"10.1101/2022.03.15.484368","title":"Photons guided by axons may enable backpropagation-based learning in the brain","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Photoreceptor and optogenetics research","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; QLT (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"MNIST database; Backpropagation; Computer science; Transmission (telecommunications); Artificial neural network; Task (project management); Artificial intelligence; Noise (video); Neuroscience; Telecommunications; Engineering; 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":[0.0002036647,0.0002429188,0.000142565,0.000138066,0.0002291632,0.0005212153,0.0004030305,0.0006893323,0.001703125],"category_scores_gemma":[0.0008046408,0.0001537365,0.0002281124,0.0001058561,0.0005577437,0.0007344242,0.0004165235,0.0005572857,0.0003168665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003495952,"about_ca_system_score_gemma":0.0003280378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009842208,"about_ca_topic_score_gemma":0.0007731237,"domain_scores_codex":[0.9999448,0.00001294194,0.000002885263,0.00001096111,0.00001874715,0.00000969706],"domain_scores_gemma":[0.9997889,0.0001003881,0.00003317119,0.00001804763,0.00003616619,0.00002334441],"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":[0.0001980461,0.0001587843,0.002530487,0.000250093,0.00006850262,0.0006708127,0.0002149786,0.4166211,0.3173765,0.2055109,0.002551952,0.0538478],"study_design_scores_gemma":[0.000011238,0.00004721726,0.0004488515,0.00001572201,0.000009226717,0.0001085656,0.00001394809,0.916926,0.03023949,0.05037663,0.001788371,0.00001482875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3606248,0.0006903469,0.6214592,0.001360724,0.0002516468,0.00003341419,0.00009515023,0.001109716,0.01437502],"genre_scores_gemma":[0.9508758,0.000310468,0.04319619,0.0001145615,0.0000204223,0.00001653571,0.00002002296,0.0000378274,0.005408152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001703125,"threshold_uncertainty_score":0.005697489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03917888989905574,"score_gpt":0.2859705988699208,"score_spread":0.246791708970865,"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."}}