{"doi":"10.1109/tmbmc.2019.2943288","title":"Sequential Bayesian Detection of Spike Activities From Fluorescence Observations","abstract":null,"journal":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","year":2019,"id":631753,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"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":860591,"name":"Bin Li","orcid":"0000-0002-6166-229X","position":1,"is_corresponding":false},{"id":34312,"name":"Weisi Guo","orcid":"0000-0003-3524-3953","position":2,"is_corresponding":false},{"id":1637319,"name":"Wenxiu Hu","orcid":null,"position":3,"is_corresponding":false},{"id":1637321,"name":"Chenglin Zhao","orcid":null,"position":4,"is_corresponding":false},{"id":1637317,"name":"Zhuangkun Wei","orcid":"0000-0002-8073-7859","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Sequential Bayesian Detection of Spike Activities From Fluorescence Observations","abstract":"Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in the combined ambient noise with fluctuated baseline, which contaminates the observations, thereby deteriorating the reliability of spike detection. This may be even worse in the face of the nonlinear biological process, the coupling interactions between spikes and baseline, and the unknown critical parameters of an underlying model, in which erroneous estimations of parameters will affect the detection of spikes causing further error propagation. The state-of-the-art MLSpike is premised on static parameter inference on spike events and ignores sequential spike nonlinear interactions. In this paper, we propose a random finite set (RFS) based Bayesian inference approach, which encapsulates the dynamics of sequential spikes, fluctuated baseline, and unknown model parameters. Specifically, the cardinal probability of RFS is able to distinguish latent spike behaviours (e.g., spike or non-spike). Our results demonstrate that the proposed scheme can gain an extra 12% detection accuracy in comparison with the state-of-the-art MLSpike method.","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W2975562611","authors":[],"funders":[{"funder_name":"Young Talent Lifting Program of China Institute of Communications","grant_id":"QT2017001","title":null},{"funder_name":"Molecular Signalling in Complex Environments","grant_id":"U.S. AFOSR: FA9550-17-1-0056","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"61971050","title":null}],"total_grants":3,"fwci":0.3593,"citation_percentile":0.59195646,"influential_citations":0,"citation_trend":[{"year":2020,"count":2},{"year":2021,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/6687308/8951037/08847451.pdf?arnumber=8847451","host_type":"publisher"},{"url":"https://doi.org/10.1109/tmbmc.2019.2943288","host_type":"journal"}],"fields_of_study":["Neural dynamics and brain function","Advanced Fluorescence Microscopy Techniques","Receptor Mechanisms and Signaling"],"mesh_terms":[],"keywords":["Spike (software development)","Computer science","Inference","Bayesian probability","Bayesian inference","Spike train","Pattern recognition (psychology)","Noise (video)","Artificial intelligence","Nonlinear system","Set (abstract data type)","Machine learning","Physics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T00:36:47.613210Z","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":[]}