Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience Series)
| Author | : | |
| Rating | : | 4.85 (675 Votes) |
| Asin | : | 0262514206 |
| Format Type | : | paperback |
| Number of Pages | : | 464 Pages |
| Publish Date | : | 2016-06-21 |
| Language | : | English |
DESCRIPTION:
(Bard Ermentrout, Department of Mathematics, University of Pittsburgh)A unique contribution to the theoretical neuroscience literature that can serve as a useful reference for audiences ranging from quantitatively skilled undergraduates interested in mathematical modeling, to neuroscientists at all levels, to graduate students and even researchers in the field of theoretical neuroscience. (Richard Fitzhugh, former researcher, Laboratory of Biophysics, National Institutes of Health) . The book will be suitable for mathematicians and physicists who want to jump into this exciting field as well as for neuroscientists who desire a deeper understanding of the utility of nonlinear dynamics applied to biology. (John Rinzel, Center for Neural Science and Courant Institute, New York University)Eugene Izhikevich has written an excellent introduct
An Interesting Book on Spiking Mechanism and Nonlinear Dynamical System Man Kam Tam The goal of Izhikevich's book is to study "the relationship between electrophysiology, bifurcations, and computational properties of neurons." The book also introduces the fundamental concepts of nonlinear dynamical system such as (1) equilibrium, (2) stability, (3) limit cycle attractor, and (4) bifurcations. Actually, it is a good introductory book on applying nonlinear dynamical system on scientific research. The primary subject of the book is the spiking (excitability and bursting) of neurons. By uti. So you think you are afraid of some math? This book encapsulates in a single text a large body of knowledge by the author and others over the past two decades on the use of geometrical techniques to both classify and study a large range of single neuron models. While much of this material is known to "experts" in the field, the value of this text is i1) teaching this dynamical systems perspective on single neuron dynamics to generations of new students and 2) educating non-mathematicians into both the utility and use of these theories. Many othe. "Amazing Book" according to Ghassan Ayesh. This book will teach you the dynamics of neurons, how to model the dynamics of neurons, complex systems modeling and how our understanding of the spiking neural systems came. This is a prize in every way. The book is engaging and easy to follow - well to some extent given the advanced topic the author is engaging the readers with. I am impressed of the ease the author applies non linear dynamical systems theory modeling techniques at ease in order to come up with a neural model that the author Izhikevich
He is editor-in-chief of Scholarpedia, the free peer-reviewedencyclopedia. Eugene M. Izhikevich is Chairman and CEO of Brain Corporation in San Diego and was formerly Senior Fellow in Theoretical Neurobiology at the Neurosciences Institute, San Diego.
It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology.Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines.Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum -- or taught by math or physics department in a way that is suitable for students of biology. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. This book offers neuroscience students and researchers a compr