Fundamentals of Computational Neuroscience / Edition 2

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Overview

Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic.

The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies.

Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book will be the essential text for anyone in the brain sciences who wants to get to grips with this topic.

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Editorial Reviews

Doody's Review Service
Reviewer: Christopher J Graver, PhD, ABPP-CN(Madigan Healthcare System)
Description: Computational neuroscience focuses on understanding different levels of brain networks from neurons to basic networks to system-level models. This book provides an introduction to the field.
Purpose: The main purpose is to provide a foundation to facilitate understanding of the more advanced scientific literature. This second edition provides updates with new ideas and models, as well as additions to make it a more user-friendly textbook for students.
Audience: The core audience for this book is students in the neurosciences and cognitive psychology. Readers in similar fields who are unfamiliar with computational neuroscience could also use this as a primer. The author is on the faculty at Dalhousie University and has published a number of articles and book chapters in the computer sciences.
Features: A brief introduction to systems and the idea of brain processing as anticipatory rather than reactive begins the book. Both technical terminology and common examples enhance understanding. Basic neuronal anatomy and functioning is covered next, with a basic introduction to computational modeling in MATLAB. Although the computer code might seem daunting at first to novice readers, an explanation of what is going into the simulation code is transparently provided. Each subsequent chapter builds in complexity both for neuronal networks and the associated modeling. Each of these chapters integrates the neuroanatomy with the computer science well. Figures and tables illustrate concepts and provide equations, as well as sample code. Appendixes provide additional foundational information on equations, calculus, basic probability, and basic information theory. An introduction to MATLAB is also provided. There is not an abundance of references, but a review of the literature was not the intent of the book. Suggestions for additional readings are provided at the end of each chapter. Additional information in terms of animations and programs also can be gained from the author's website.
Assessment: As an introductory and foundational text for students, this book works quite well. It is well organized, easy to reference, and uncomplicated as far as this technical topic goes. The neuroanatomy is truly integrated with computer modeling and includes real-world examples of the phenomena to place a concrete face on an otherwise abstract body. The second edition includes many new features that make the update worthwhile.
From the Publisher
"As an introductory and foundational text for students, this book works quite well. It is well organized, easy to reference, and uncomplicated as far as this technical topic goes. The neuroanatomy is truly integrated with computer modeling and includes real-world examples of the phenomena to place a concrete face on an otherwise abstract body. The second edition includes many new features that make the udpate worthwhile."—Doody's
From The Critics
Reviewer: Christopher J. Graver, PhD (Madigan Army Medical Center)
Description: Computational neuroscience focuses on understanding different levels of brain networks from neurons to basic networks to system-level models. This book provides an introduction to the field.
Purpose: The main purpose is to provide a foundation to facilitate understanding of the more advanced scientific literature. This second edition provides updates with new ideas and models, as well as additions to make it a more user-friendly textbook for students.
Audience: The core audience for this book is students in the neurosciences and cognitive psychology. Readers in similar fields who are unfamiliar with computational neuroscience could also use this as a primer. The author is on the faculty at Dalhousie University and has published a number of articles and book chapters in the computer sciences.
Features: A brief introduction to systems and the idea of brain processing as anticipatory rather than reactive begins the book. Both technical terminology and common examples enhance understanding. Basic neuronal anatomy and functioning is covered next, with a basic introduction to computational modeling in MATLAB. Although the computer code might seem daunting at first to novice readers, an explanation of what is going into the simulation code is transparently provided. Each subsequent chapter builds in complexity both for neuronal networks and the associated modeling. Each of these chapters integrates the neuroanatomy with the computer science well. Figures and tables illustrate concepts and provide equations, as well as sample code. Appendixes provide additional foundational information on equations, calculus, basic probability, and basic information theory. An introduction to MATLAB is also provided. There is not an abundance of references, but a review of the literature was not the intent of the book. Suggestions for additional readings are provided at the end of each chapter. Additional information in terms of animations and programs also can be gained from the author's website.
Assessment: As an introductory and foundational text for students, this book works quite well. It is well organized, easy to reference, and uncomplicated as far as this technical topic goes. The neuroanatomy is truly integrated with computer modeling and includes real-world examples of the phenomena to place a concrete face on an otherwise abstract body. The second edition includes many new features that make the update worthwhile.
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Product Details

  • ISBN-13: 9780199568413
  • Publisher: Oxford University Press
  • Publication date: 1/18/2010
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 416
  • Product dimensions: 7.40 (w) x 9.60 (h) x 0.90 (d)

Table of Contents

1. Introduction
Basic Neurons
2. Neurons and conductance-based models
3. Simplified neuron and population models
4. Associators and synaptic plasiticity
Basic Networks
5. Cortical organizations and simple networks
6. Feed-forward mapping networks
7. Cortical feature maps and competitive population coding
8. Recurrent associative networks and episodic memory
System-Level Models
9. Modular networks, motor control, and reinforcement learning
10. The cognitive brain
A. Some useful mathematics
B. Numerical calculus
C. Basic probability theory
D. Basic information theory
E. A brief introduction to MATLAB

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