{"id":"W2999250520","doi":"10.1016/j.nlm.2020.107164","title":"Locating the engram: Should we look for plastic synapses or information-storing molecules?","year":2020,"lang":"en","type":"review","venue":"Neurobiology of Learning and Memory","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Engram; Connectionism; Cognitive science; Neuroscience; Psychology; Cognition; Associative property; Content-addressable memory; Synapse; Neural substrate; Biological neural network; Artificial neural network; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003014839,0.0003866132,0.001027614,0.0001355308,0.0004367664,0.00003965242,0.0004205887,0.0002687313,0.00002485665],"category_scores_gemma":[0.005868265,0.0002317638,0.0002145907,0.0002348414,0.0002466204,0.0001207932,0.0001980641,0.001025701,0.00002750808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001450396,"about_ca_system_score_gemma":0.0001622484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.640529e-7,"about_ca_topic_score_gemma":4.491142e-7,"domain_scores_codex":[0.9977512,0.0006726009,0.0006406308,0.0004711538,0.0001176932,0.0003467377],"domain_scores_gemma":[0.9926534,0.006308327,0.0007130252,0.0002020914,0.00003417894,0.00008901995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001100866,0.00002034905,0.000001064481,0.01539212,0.00003380906,0.00002077311,0.0004375535,0.00009312564,0.005959305,0.0004368163,0.0001240089,0.977371],"study_design_scores_gemma":[0.0002846477,0.000920848,1.99926e-7,0.001959528,0.0003016494,0.0003065409,0.0002261489,0.0002250513,0.01490302,0.00002376007,0.9805212,0.0003274013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0031446,0.9874406,0.001475118,0.000579481,0.003407633,0.002929115,0.0001003968,0.0003796853,0.0005433962],"genre_scores_gemma":[0.002897062,0.995796,0.0001143483,0.000378127,0.0001814725,0.0001638325,0.00001808645,0.0000454335,0.0004056141],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9803972,"threshold_uncertainty_score":0.9451053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1602175533943845,"score_gpt":0.3637187637245581,"score_spread":0.2035012103301736,"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."}}