{"id":"W2122985363","doi":"10.1162/neco_a_00734","title":"Surrogate Population Models for Large-Scale Neural Simulations","year":2015,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surrogate model; Artificial neural network; Computer science; Population; Spike (software development); Models of neural computation; Biological neuron model; Computation; Gaussian; Surrogate data; Algorithm; Artificial intelligence; Nonlinear system; Machine learning","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":[0.001060631,0.0006776443,0.001021483,0.0006590318,0.0005833874,0.001022637,0.001554622,0.002014903,0.003535961],"category_scores_gemma":[0.0054719,0.0006146489,0.001136305,0.0006940971,0.0009671864,0.001467643,0.001256798,0.001990582,0.0006124399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003856,"about_ca_system_score_gemma":0.0008649037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003394152,"about_ca_topic_score_gemma":0.002794075,"domain_scores_codex":[0.9996728,0.0001483964,0.00001856485,0.00003289994,0.00009938484,0.00002791808],"domain_scores_gemma":[0.9982682,0.001206579,0.0001208859,0.0001302273,0.0001794687,0.00009457976],"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":[0.000009724266,0.000008672489,0.0001336308,0.00001685671,0.0000115489,0.00002619449,0.00001759727,0.9742717,0.0003677495,0.0235316,0.0002306093,0.001374125],"study_design_scores_gemma":[0.000001702388,0.000001678539,0.00001114673,0.000001764968,7.763146e-7,0.000002910226,0.000001672186,0.9936171,0.00004394786,0.006116921,0.0001989467,0.00000146244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01049353,0.0002482269,0.9844425,0.0002659087,0.00004311403,0.00003780844,0.000121495,0.0002788464,0.004068667],"genre_scores_gemma":[0.6421437,0.001143894,0.3435265,0.0003043147,0.0001218345,0.001151087,0.0006511272,0.0005011893,0.01045629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003535961,"threshold_uncertainty_score":0.01182896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08651826119971792,"score_gpt":0.315789880222502,"score_spread":0.2292716190227841,"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."}}