Remodeling the light-adapted electroretinogram using a bayesian statistical approach.

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Bibliographic Details
Title: Remodeling the light-adapted electroretinogram using a bayesian statistical approach.
Authors: Brabec M; Institute of Computer Science, Czech Academy of Sciences, Pod Vodarenskou Vezi 2, Prague 8, 182 00, Czech Republic.; National Institute of Public Health, Srobarova 48, Prague 10, 100 00, Czech Republic., Marmolejo-Ramos F; College of Education, Psychology, and Social Work, Flinders University, Adelaide, Australia., Loh L; College of Nursing and Health Sciences, Flinders University, Caring Futures Institute, Adelaide, Australia., Lee IO; Behavioural and Brain Sciences Unit, Population Policy and Practice Programme, Great Ormond Street Institute of Child Health, University College London, University College London, London, UK., Kulyabin M; Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universitat Erlangen-Nurnberg, 91058, Erlangen, Germany., Zhdanov A; VisioMed.AI, Moscow, Russia., Posada-Quintero H; Biomedical Engineering Department, University of Connecticut, Storrs, CT, 06269, USA., Thompson DA; Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.; Great Ormond Street Institute for Child Health, University College London, University College London, London, UK., Constable PA; College of Nursing and Health Sciences, Flinders University, Caring Futures Institute, Adelaide, Australia. Paul.constable@flinders.edu.au.
Source: BMC research notes [BMC Res Notes] 2025 Jan 23; Vol. 18 (1), pp. 33. Date of Electronic Publication: 2025 Jan 23.
Publication Type: Journal Article
Journal Info: Publisher: Biomed Central Country of Publication: England NLM ID: 101462768 Publication Model: Electronic Cited Medium: Internet ISSN: 1756-0500 (Electronic) Linking ISSN: 17560500 NLM ISO Abbreviation: BMC Res Notes Subsets: MEDLINE
Database: MEDLINE Ultimate
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