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Computational Physiology: Simula Summer School 2023 − Student Reports: Simula SpringerBriefs on Computing, cartea 17

Editat de Kimberly J. McCabe
en Limba Engleză Paperback – mar 2024
This open access volume compiles student reports from the 2023 Simula Summer School in Computational Physiology. The reports provide an overview of some tools available to model physiology in excitable tissues across scales and scientific questions.  
In 2023, Simula held the nineth annual Summer School in Computational Physiology in collaboration with the University of Oslo (UiO) and the University of California, San Diego (UCSD). Each year, the course focuses on modeling excitable tissues, with a special interest in cardiac physiology and neuroscience. Group research projects conducted by graduate students from around the world result in reports addressing problems of physiological importance. Reports may not necessarily represent new scientific results; rather, they can reproduce or supplement earlier studies.
Reports from seven of the summer projects are included as separate chapters. The topics represented include multiscale mechanics, electrophysiology, pharmacology, and machine learning. 
This is an open access book.
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Specificații

ISBN-13: 9783031531446
ISBN-10: 3031531442
Pagini: 110
Ilustrații: XIV, 110 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.19 kg
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Simula SpringerBriefs on Computing, Reports on Computational Physiology

Locul publicării:Cham, Switzerland

Cuprins

1 Studying the role of astrocytic membrane properties on microscopic fluid flow in brain tissue.- 2 Computational modeling of ephaptic coupling in myelinated and unmyelinated axon bundles using the EMI framework.- 3 Augmentation of cardiac ischemic geometry for improving machine learning performance in arrhythmic risk stratification.- 4 Non-Invasive Detection of Fetal Ischemia Through Electrocardiography.- 5 Reconstruction of a Pancreatic Beta Cell Network from Heterogeneous Functional Measurements.- 6 The Impact of Mechano-Electric Feedback on Drug- and Stretch-Induced Arrhythmia using a Computational Model of Cardiac Electromechanics.- 7 Impact of modeling assumptions on hemodynamic stresses in predicting cerebral aneurysm rupture status.- 8 Random Path Generation with Spiking Neural Networks.



Caracteristici

Book describes exiting new research on brain and heart diseases Cutting edge computational science methods Can be used for teaching at graduate level This book is open access, which means that you have free and unlimited access

Textul de pe ultima copertă

This open access volume compiles student reports from the 2021 Simula Summer School in Computational Physiology. Interested readers will find herein a number of modern approaches to modeling excitable tissue. This should provide a framework for tools available to model subcellular and tissue-level physiology across scales and scientific questions. In June through August of 2021, Simula held the seventh annual Summer School in Computational Physiology in collaboration with the University of Oslo (UiO) and the University of California, San Diego (UCSD). The course focuses on modeling excitable tissues, with a special interest in cardiac physiology and neuroscience. The majority of the school consists of group research projects conducted by Masters and PhD students from around the world, and advised by scientists at Simula, UiO and UCSD. Each group then produced a report that addreses a specific problem of importance in physiology and presents a succinct summary of the findings. Reportsmay not necessarily represent new scientific results; rather, they can reproduce or supplement earlier computational studies or experimental findings.
Reports from eight of the summer projects are included as separate chapters. The fields represented include cardiac geometry definition (Chapter 1), electrophysiology and pharmacology (Chapters 2–5), fluid mechanics in blood vessels (Chapter 6), cardiac calcium handling and mechanics (Chapter 7), and machine learning in cardiac electrophysiology (Chapter 8).