{"doi":"10.1155/2010/426539","title":"State-Space Algorithms for Estimating Spike Rate Functions","abstract":"<jats:p>The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.</jats:p>","journal":"Computational Intelligence and Neuroscience","year":2010,"id":613098,"datarank":0.5289540786924243,"base_score":3.5263605246161616,"endowment":3.5263605246161616,"self_citation_contribution":0.5289540786924243,"citation_network_contribution":0.0,"self_endowment_contribution":0.5289540786924243,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":33,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1579206,"name":"Joao D. Scalon","orcid":null,"position":1,"is_corresponding":false},{"id":777228,"name":"Sylvia Wirth","orcid":"0000-0001-7002-329X","position":2,"is_corresponding":false},{"id":1579207,"name":"Marianna Yanike","orcid":null,"position":3,"is_corresponding":false},{"id":573524,"name":"Wendy A. Suzuki","orcid":"0000-0003-2205-7377","position":4,"is_corresponding":false},{"id":196053,"name":"Emery N. Brown","orcid":null,"position":5,"is_corresponding":false},{"id":635355,"name":"Anne C. Smith","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"State-Space Algorithms for Estimating Spike Rate Functions","abstract":"<jats:p>The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.</jats:p>","is_dataset_classified":null,"base_score":3.5263605246161616,"endowment":3.5263605246161616,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19911062","pmcid":"PMC2774470","openalex_id":"https://openalex.org/W2097948446","authors":[],"funders":[{"funder_name":"National Institute on Drug Abuse","grant_id":"DA015644","title":null},{"funder_name":"National Institute on Drug Abuse","grant_id":"DA01564","title":null},{"funder_name":"National Institute on Drug Abuse","grant_id":"MH59733","title":null},{"funder_name":"National Institute on Drug Abuse","grant_id":"MH61637","title":null},{"funder_name":"National Institute on Drug Abuse","grant_id":"MH071847","title":null},{"funder_name":"National Institute on Drug Abuse","grant_id":"DP1 OD003646-01","title":null},{"funder_name":"National Institute on Drug Abuse","grant_id":"MH58847","title":null},{"funder_name":"NIDA NIH HHS","grant_id":"R01 DA015644","title":null},{"funder_name":"NIH HHS","grant_id":"OD003646-01","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R01 MH058847","title":null},{"funder_name":"NIH HHS","grant_id":"DP1 OD003646","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R01 MH071847","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R01 MH059733","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"K02 MH061637","title":null}],"total_grants":14,"fwci":0.7795,"citation_percentile":0.70665956,"influential_citations":0,"citation_trend":[{"year":2012,"count":3},{"year":2013,"count":7},{"year":2014,"count":3},{"year":2015,"count":1},{"year":2016,"count":1},{"year":2017,"count":2},{"year":2018,"count":1},{"year":2019,"count":2},{"year":2020,"count":3},{"year":2021,"count":1},{"year":2022,"count":1},{"year":2023,"count":1},{"year":2024,"count":3},{"year":2025,"count":1}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://downloads.hindawi.com/journals/cin/2010/426539.pdf","host_type":"journal"},{"url":"https://downloads.hindawi.com/journals/cin/2010/426539.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/cin/2010/426539.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/cin/2010/426539.xml","host_type":"publisher"},{"url":"https://doi.org/10.1155/2010/426539","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/19911062","host_type":"repository"},{"url":"http://doi.org/10.1155/2010/426539.","host_type":"repository"},{"url":"https://doaj.org/article/fc4bd2f8a0ba4d888374f02fdb6c7ebc","host_type":"repository"},{"url":"http://hdl.handle.net/1721.1/50244","host_type":"repository"},{"url":"http://repositorio.ufla.br/jspui/handle/1/38051","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2774470","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC2774470","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC2774470?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Neural dynamics and brain function","Neural Networks and Applications","Blind Source Separation Techniques"],"mesh_terms":["Action Potentials","Algorithms","Animals","Association Learning","Bayes Theorem","Computer Simulation","Electrophysiology","Eye Movements","Hippocampus","Macaca","Models, Neurological","Neurons","Regression Analysis","Models, Statistical","Confidence Intervals"],"keywords":["Smoothing","Spike (software development)","Spline (mechanical)","Computer science","Algorithm","Smoothing spline","State space","Bayesian probability","Spike train","Range (aeronautics)","State-space representation","Mathematics","Artificial intelligence","Statistics","Spline interpolation"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T06:35:50.909603Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}