{"id":"W4389850796","doi":"10.1007/s11571-023-10031-7","title":"Modelling neural probabilistic computation using vector symbolic architectures","year":2023,"lang":"en","type":"article","venue":"Cognitive Neurodynamics","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Neuro Research Charitable Trust; National Coordination Office; Ontario Innovation Trust","keywords":"Probabilistic logic; Computer science; Connectionism; Theoretical computer science; Probability distribution; Artificial intelligence; Probability density function; Kernel density estimation; Entropy (arrow of time); Artificial neural network; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001131519,0.0002668838,0.0002185085,0.0003038061,0.0003650993,0.0001270061,0.0001654257,0.00006956214,0.000005700283],"category_scores_gemma":[0.0007208922,0.0002627331,0.0001115486,0.001072488,0.000166681,0.0001209557,0.0001275867,0.0003599065,0.00007010623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005140392,"about_ca_system_score_gemma":0.00004048711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001599193,"about_ca_topic_score_gemma":0.000005314354,"domain_scores_codex":[0.9979257,0.0002292394,0.0002902678,0.0007189019,0.0003528295,0.0004830424],"domain_scores_gemma":[0.9986222,0.0009031271,0.0001338192,0.0001443621,0.0000865785,0.0001098851],"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":[0.00005737253,0.00003850713,0.0001003175,0.00003307635,0.00000361249,0.00007606948,0.0001085539,0.9288597,0.0668795,0.0009777696,0.000004898183,0.00286066],"study_design_scores_gemma":[0.0003150502,0.0001091785,0.002228572,0.00003623122,0.00002983782,0.00007377458,0.00002475416,0.9903511,0.0008465031,0.005716006,0.000006515848,0.000262407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960153,0.000004663284,0.03731665,0.0001780811,0.0009270504,0.0005432226,0.00009030486,0.0004999054,0.0002871412],"genre_scores_gemma":[0.9989581,0.00001123054,0.00008560147,0.0006002778,0.0001121992,0.00002065181,0.00005427227,0.00006857159,0.00008915074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.066033,"threshold_uncertainty_score":0.9999825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07107184893808045,"score_gpt":0.2954904424616859,"score_spread":0.2244185935236054,"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."}}