{"id":"W2032217783","doi":"10.1109/ccece.2013.6567778","title":"A self exciting point process model for neural spike sequences, and its rate estimation","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University of British Columbia","keywords":"Estimator; Poisson distribution; Point process; Spike train; Computer science; Mathematics; Statistics; Spike (software development); Applied mathematics; Algorithm","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":[0.002802527,0.0006812545,0.0008259667,0.000769645,0.0002886139,0.0009717631,0.002050771,0.001817563,0.001348547],"category_scores_gemma":[0.008672561,0.0005016929,0.000870061,0.001267721,0.001047313,0.001673295,0.0006676434,0.001476858,0.000546239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006246911,"about_ca_system_score_gemma":0.0007129601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001851787,"about_ca_topic_score_gemma":0.00141705,"domain_scores_codex":[0.9991929,0.0003214452,0.00004243634,0.0002034403,0.0001952507,0.00004453911],"domain_scores_gemma":[0.9974211,0.001650682,0.0003173399,0.0002150631,0.0003287666,0.00006702571],"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":[0.0001256408,0.00005970821,0.00213534,0.0001401723,0.00005652602,0.0002563512,0.0001398059,0.8166901,0.008683026,0.1403105,0.0008215139,0.03058129],"study_design_scores_gemma":[0.000004139602,0.0000179315,0.000181621,0.000003744379,0.000005362543,0.00004552652,0.000003620693,0.9890114,0.000352778,0.01007493,0.0002900485,0.000008848288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009848754,0.0001206609,0.9893434,0.000101821,0.00001730901,0.00003040858,0.00005235734,0.00005754949,0.0004277706],"genre_scores_gemma":[0.6474511,0.0009013643,0.3416139,0.0001869863,0.0001864724,0.000500188,0.0003867445,0.0001078195,0.008665227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002802527,"threshold_uncertainty_score":0.01482135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03936261198926992,"score_gpt":0.2756075666339693,"score_spread":0.2362449546446994,"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."}}