**Hardcover**| Out of Print | ISBN: 9780262090438 | 464 pp. | 7 x 10 in | 409 illus.| July 2006

**Paperback**|

**$38.00 Text**|

**£26.95**| ISBN: 9780262514200 | 464 pp. | 7 x 10 in | 409 illus.| January 2010

**eBook**|

**$27.00 Text**| ISBN: 9780262252324 | 464 pp. | 409 illus.| January 2010

About MIT Press eBooks

## Dynamical Systems in Neuroscience

## Overview

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.

## About the Author

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. He is editor-in-chief of Scholarpedia, the free peer-reviewed

encyclopedia.

## Reviews

**Jonathan E. Rubin**,

*Mathematical Review*

## Endorsements

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**John Rinzel**, Center for Neural Science and Courant Institute, New York University

—

**Bard Ermentrout**, Department of Mathematics, University of Pittsburgh

—

**Richard Fitzhugh**, former researcher, Laboratory of Biophysics, National Institutes of Health