Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience) Review

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(More customer reviews)Great book for the theorist and experimentalist! I used the section on Epilepsy and the Neural Code for a grant I wrote. This book is a great reference and time spent reading it is very well rewarded. I bought the 1st & 2nd editions which are very different. Both editions are worth buying if one is involved with computer modeling, computation, mathematics, and plain old fashion recording neurophysiology.

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Much research focuses on the question of how information is processed innervous systems, from the level of individual ionic channels to large-scale neuronalnetworks, and from "simple" animals such as sea slugs and flies to cats andprimates. New interdisciplinary methodologies combine a bottom-up experimentalmethodology with the more top-down-driven computational and modeling approach. Thisbook serves as a handbook of computational methods and techniques for modeling thefunctional properties of single and groups of nerve cells.The contributors highlightseveral key trends: (1) the tightening link between analytical/numerical models andthe associated experimental data, (2) the broadening of modeling methods, at boththe subcellular level and the level of large neuronal networks that incorporate realbiophysical properties of neurons as well as the statistical properties of spiketrains, and (3) the organization of the data gained by physical emulation of thenervous system components through the use of very large scale circuit integration(VLSI) technology.The field of neuroscience has grown dramatically since the firstedition of this book was published nine years ago. Half of the chapters of thesecond edition are completely new; the remaining ones have all been thoroughlyrevised. Many chapters provide an opportunity for interactive tutorials andsimulation programs. They can be accessed via Christof Koch's Website.Contributors :Larry F. Abbott, Paul R. Adams, Hagai Agmon-Snir, James M. Bower, Robert E. Burke,Erik de Schutter, Alain Destexhe, Rodney Douglas, Bard Ermentrout, FabrizioGabbiani, David Hansel, Michael Hines, Christof Koch, Misha Mahowald, Zachary F.Mainen, Eve Marder, Michael V. Mascagni, Alexander D. Protopapas, Wilfrid Rall, JohnRinzel, Idan Segev, Terrence J. Sejnowski, Shihab Shamma, Arthur S. Sherman, PaulSmolen, Haim Sompolinsky, Michael Vanier, Walter M. Yamada.

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Tutorial on Neural Systems Modeling Review

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(More customer reviews)We used this book for the upper-level undergraduate students in an interdisciplinary computational neuroscience course at a small liberal arts college. This is a fine, well-written book. One of the strengths of this book is that it starts with very basic programming in Matlab, so that the students without programming backgrounds can easily follow along. The programs in the book are well commented, and they progress slowly and logically in complexity. More advanced math topics are well separated out in Math Boxes. The examples of the neural systems are covered in enough (but not too much) details to be interesting and accessible to the readers. The book is definitely considerate of and sensitive to the wonderfully interdisciplinary nature of this field, so that the materials can be digested by people with different backgrounds. One can not contain all the topics in computational neuroscience in a single book, but this book does a great job of covering many important and interesting ideas/areas (Hebbian learning, Hopfield model, lateral inhibition, adaptation, supervised and unsupervised learning, etc.).
It works very well as an introductory textbook (or tutorial) of the field. The codes and the discussions are clear and simple (not intended as an advanced textbook), and to me, that's the strength and unique quality of this book.
By the way, most of the computer programs listed in the book work well with Octave (as well as Matlab).

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Neural systems models are elegant conceptual tools that provide satisfying insight into brain function. The goal of this new book is to make these tools accessible. It is written specifically for students in neuroscience, cognitive science, and related areas who want to learn about neural systems modeling but lack extensive background in mathematics and computer programming.The book opens with an introduction to computer programming. Each of twelve subsequent chapters presents a different modeling paradigm by describing its basic structure and showing how it can be applied in understanding brain function. The text guides the reader through short, simple computer programs printed in the book and available by download at the companion website that implement the paradigms and simulate real neural systems. Motivation for the simulations is provided in the form of a narrative that places specific aspects of neural system behavior in the context of more general brain function. The narrative integrates instruction for using the programs with description of neural system function, and readers can actively experience the fun and excitement of doing the simulations themselves. Designed as a hands-on tutorial for students, this book also serves instructors as both a teaching tool and a source of examples and exercises that provide convenient starting points for more in-depth exploration of topics of their own specific interest.The distinguishing pedagogical feature of this book is its computer programs, written in MATLAB, that help readers develop basic skill in the area of neural systems modeling. (All of the program files are available online via the book s companion website. Actual data on real neural systems is presented in the book for comparison with the results of the simulations. Also included are asides ( Math Boxes ) that present mathematical material that is relevant but not essential to running the programs. Exercises and references at the end of each chapter invite readers to explore each topic area on their own.

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