{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001430797,0.0001554511,0.0001488017,0.0001172987,0.0002614815,0.0001158839,0.000106061,0.00006423052,0.000004158149],"category_scores_gemma":[0.0002001973,0.0001506198,0.00008227364,0.0003045876,0.00001749785,0.0007818929,0.0000401189,0.0001103472,0.00001089298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006758729,"about_ca_system_score_gemma":0.00001618792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729167,"about_ca_topic_score_gemma":0.00005384057,"domain_scores_codex":[0.9985937,0.0001211605,0.0002866727,0.0004086216,0.0003046866,0.000285093],"domain_scores_gemma":[0.9991894,0.0002665463,0.0001417488,0.0001292621,0.0001515941,0.0001214765],"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":[0.00008088029,0.0000503734,0.0004182633,0.000009138834,0.000001227391,0.000001329697,0.0001609497,0.9844511,0.008326018,0.002526065,0.0002607706,0.003713884],"study_design_scores_gemma":[0.0009317857,0.0001678285,0.001494659,0.000003732794,0.00001106406,0.000009254843,0.00002586172,0.9697118,0.0009015538,0.02644697,0.0001380258,0.0001574949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861923,0.00000690494,0.1109626,0.0007160851,0.001074016,0.0005703431,0.00008454893,0.0002161687,0.0001769999],"genre_scores_gemma":[0.9977139,6.843852e-7,0.000853645,0.0007730174,0.0001646248,0.00002072791,0.0002843487,0.00002865237,0.0001604126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1115215,"threshold_uncertainty_score":0.6142098,"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."}}