Springer Dynamics of Neural Networks: A Mathematical and Clinical Approach

This book treats essentials from neurophysiology (Hodgkin–Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing on EEG generation, ischaemia, epilepsy and neurostimulation. The book is based on a graduate course taught by clinicians and mathematicians at the Institute of Technical Medicine at the University of Twente. Throughout the text, the author presents examples of neurological disorders in relation to applied mathematics to assist in disclosing various fundamental properties of the clinical reality at hand.  Exercises are provided at the end of each chapter; answers are included. Basic knowledge of calculus, linear algebra, differential equations and familiarity with MATLAB or Python is assumed. Also, students should have some understanding of  essentials of (clinical) neurophysiology, although most concepts are summarized in the first chapters. The audience includes advanced undergraduate or graduate students in Biomedical Engineering, Technical Medicine and Biology. Applied mathematicians may find pleasure in learning about the neurophysiology and clinic essentials  applications.  In addition, clinicians with an interest in dynamics of neural networks may find this book useful, too. From the Back Cover This book treats essentials from neurophysiology (Hodgkin–Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing on EEG generation, ischaemia, epilepsy and neurostimulation. The book is based on a graduate course taught by clinicians and mathematicians at the Institute of Technical Medicine at the University of Twente. Throughout the text, the author presents examples of neurological disorders in relation to applied mathematics to assist in disclosing various fundamental properties of the clinical reality at hand.  Exercises are provided at the end of each chapter; answers are included. Basic knowledge of calculus, linear algebra, differential equations and familiarity with MATLAB or Python is assumed. Also, students should have some understanding of  essentials of (clinical) neurophysiology, although most concepts are summarized in the first chapters. The audience includes advanced undergraduate or graduate students in Biomedical Engineering, Technical Medicine and Biology. Applied mathematicians may find pleasure in learning about the neurophysiology and clinic essentials  applications.  In addition, clinicians with an interest in dynamics of neural networks may find this book useful, too. About the Author Michel van Putten studied Medicine in Leiden and Applied Physics in Delft. In 2000, he got his Ph.D. in Applied Physics from Delft, and registered as a  neurologist/clinical neurophysiologist. Michel van Putten is heading the Department of Clinical Neurophysiology of the Medisch Spectrum Twente, a large teaching hospital, and he chairs the Clinical Neurophysiology (CNPH) group at the University of Twente (utwente.nl).He is co-Founder of Clinical Science Systems, a company that develops  clinical EEG software. His research involves the pathophysiology of epilepsy, ischaemia, brain monitoring in the ICU and fundamentals of EEG generation.