Product Description
In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. 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. 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. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. 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 neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

CUSTOMER REVIEWS (Average Customer Rating: 5.0 based on 6 reviews)

Great book for the mathematics of neuroscience by Arij Daou (Tallahassee, FL USA)

5 Stars
October 28, 2009
Being a biomathematician and neuroscientist, I found that Izhikevich's book "Dynamical Systems in Neuroscience" is a great reference to broaden my understanding of mathematical neuroscience and neurophysiology, and in particular, neural modeling, nonlinear dynamics and the mathematics involved between the brief bursts of neural activity. I recommend it to every neuroscientist in the field.

Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience) by Susumu Kobayashi (Mito-shi, Japan)

5 Stars
April 21, 2009
I could know the new model of human brain which I have not ever seen. It is very interesting.

Beautiful book of dynamical system of neurons by Il Park

5 Stars
February 22, 2009
This is an excellent book on application of 2-D dynamical system theory to (minimal) spiking neuron models. I highly recommend it for electrophysiologist who wants to learn more about what they observe, and to computational neuroscientists in general. Prior exposure to dynamical systems and neuroscience is helpful.

Book on the dynamcis of neurons by CHINKIONG QUEK

4 Stars
May 21, 2008
This book gives an understanding of how the dynamics of neurons work. It gives an insight to many different types of models. However, you would require a neuroscience background before understanding it more in depth.

So you think you are afraid of some math? by Robert Butera (Pine Lake, GA)

5 Stars
October 24, 2007
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 other texts and papers on this topic leave non-mathematicians "in the dust" shortly after the introduction, but Eugene's excellent use of figures to explain concepts geometrically as well as mathematically enables a PhD student in engineering or quantitative biology to fully appreciate what is going on, not to mention seasoned experimentalists.

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