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Christoph Börgers - Introduction to Modeling Neuronal Dynamics, Inbunden

Inbunden, Engelska, 2017

Introduction to Modeling Neuronal Dynamics

Av Christoph Börgers

Del 66 i serien Texts in Applied Mathematics

813 kr

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Beskrivning
This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology.Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity.An extensive online collection of Matlab programs generating the figures accompanies the book.
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Christoph Börgers is Professor of Mathematics at Tufts University, USA.  His expertise is in mathematical neuroscience, applied dynamical systems, numerical analysis, and scientific computing.

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“I would be happy to recommend this textbook to instructors seeking a textbook for an introductory course in mathematical neuroscience, and for students with quantitative backgrounds interested in taking such a course. … For those students beginning their climb from the established foundations of mathematical neuroscience towards these newer branches, An Introduction to Modeling Neuronal Dynamics offers a fine place to start.” (Braden A. W. Brinkman, SIAM Review, Vol. 61 (2), 2019)“The book is supplemented with a helpful library of Matlab scripts that generate many of the plots and numerical results presented in it. The exercises encourage the readers to modify the scripts to create new plots ... the book takes the reader on a fascinating journey of neuronal dynamics via introduction, analysis and numerical simulation of relevant dynamical systems. It is written with clarity and rigor.” (Oksana Bihun, zbMATH 1382.92001, 2018)

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