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.

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

Inhalt

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.