Neuronal Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neuronal Dynamics write by Wulfram Gerstner. This book was released on 2014-07-24. Neuronal Dynamics available in PDF, EPUB and Kindle. This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Neuronal Dynamics
Neuronal Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neuronal Dynamics write by Wulfram Gerstner. This book was released on 2014-07-24. Neuronal Dynamics available in PDF, EPUB and Kindle. What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin–Huxley equations and Hopfield model, as well as modern developments in the field such as generalized linear models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.
Neuronal Dynamics
Neuronal Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neuronal Dynamics write by Wulfram Gerstner. This book was released on 2014-07-24. Neuronal Dynamics available in PDF, EPUB and Kindle. What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin-Huxley equations and Hopfield model, as well as modern developments in the field such as generalized linear models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.
An Introduction to Modeling Neuronal Dynamics
An Introduction to Modeling Neuronal Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Introduction to Modeling Neuronal Dynamics write by Christoph Börgers. This book was released on 2017-04-17. An Introduction to Modeling Neuronal Dynamics available in PDF, EPUB and Kindle. 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.
Dynamical Systems in Neuroscience
Dynamical Systems in Neuroscience - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamical Systems in Neuroscience write by Eugene M. Izhikevich. This book was released on 2010-01-22. Dynamical Systems in Neuroscience available in PDF, EPUB and Kindle. Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. 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.
