Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience) by Izhikevich, Eugene M. (2010) Paperback Paperback – January 22, 2010

The author's incessant focus on providing geometrical insight into the mathematics is the most astonishing feature of this book. I cannot imagine a better introduction to quantitative and qualitative understanding of the dynamics of individual neurons.I read this book cover to cover (including ch10 which is online on the author's website) and it never got boring. This book gives you "the big picture" about neuron dynamics.The author also does an exquisite job at classifying different neuron models and showing "what really matters" about them. And at every step, information is provided about how to reproduce the figures (which is easy with MATLAB, Mathematica, or similar tools) so that you can verify your understanding and play around. There are tons of examples of specific neuron recordings and explanations in terms of the models being discussed.The preface and ch1 are available on the author's website[...]As a point of reference, Dayan & Abbott "Theoretical Neuroscience" has broader and deeper coverage of "computational neuroscience" than Izhikevich, but does not have the same kind of deep qualitative geometrical insight into neuron dynamics. I would consider it as a next step after reading this book.As a rule of thumb, if you are uncomfortable with the idea of using MATLAB, Mathematica, or a similar tool to plot a vector field or integrate a system of ODE, you will probably not benefit from the quantitative side of this book. But the book may still be useful for developing qualitative understanding.

My advisor lent it to me for a few weeks. I bought it the next week I gave it back. It has incredible amount of information. The equations make it easier to create customized models.

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 evolves throughout the chapters of the book, with clear schematics in every chapter which visually explain the modeling as well. Superb indeed. The subject overall is not an easy topic to attack or explain but Izhikevich is up for the challenge.

The book claims to be an 'introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience'. Potential purchasers need to be warned that it provides a highly mathematical account of neuroscience that those coming from psychology or biology might find challenging. The challenge is not eased by the approach taken by the author, who clearly has little sympathy for any reader who finds mathematical equations a pain. The cartoon on page 7 says it all. One character dressed in a tee shirt (presumably a researcher) is telling his senior 'I developed the most realistic neural networks'. His senior, dressed in suit and tie, replies 'I do not see any neurons here, only equations'. This book will be a joy to all who do not want to see neurons, only mathematical equations.

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 utilizing graphs or phase portraits to demonstrate the mechanism of the spiking generation of neurons, the author makes the readers understand both the spiking mechanism and the concepts of nonlinear dynamical system with ease.

This qualifies as a "bible" of compuational neuroscience, but it is not a beginner's book. I had the good fortune to have lunch with the author and he is one of the best in the field. The book is well written and does a great job of providing an overview of applying non-linear dynamics to neuroscience. The mathematical concepts are explained well and in sufficient detail for the punctilious. I bought a second copy to keep at work and it will become a go-to manual for me of sorts.

j'en suis contente

The author's method (of looking for chaotic attractors first in neurons, and then in oscillators of neurons) may be the key to understanding real brains.

Good

The author has done an extraordinary ordering work about neuron behaviour models and has a great capability to explain complex topics with simple words. Moreover, despite the Nonlinear dynamics is a hard subject, the author guides with your hand to understand from the fundamentals to the advanced concepts.The book doesn't include networks chapters, but just some paragraphs.It is not about simulations, although it proposes matlab scripts downloadable and very clear way to set up a simulation.Remarkable: you can also use this book to just learn quite well nonlinear dynamics from zero.

This is a very good introductory book for the analysis of neurons from the perspective of dynamical systems. It is very well written, and introduces very well the various mathematical concepts. Exercises at the end of the chapters are also useful.The informations about complex neuronal networks, insetad, is very limited and the interested reader should use other references (I got the impression that dynamic systems perspective is useful when treating one, maximum two neurons; with small networks the story probably becomes too complex with this approach).