Fundamentals of Neural Networks

Book Description

Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.

Customer Reviews:

The best book to get introduced to Neural Networks.......2007-07-11

This is the book I used in my AI class. I have found it very well written and interesting to read and go through the very first neural networks models such as the Hebb net, the perceptron and the Adaline. Then the book continues by presenting simple neural network applications like pattern association.

I remember that our professor did ask the class to do one of the proposed projects in the pattern association chapter which consisted of implementing a small OCR with a neural network and this exercise did really help to better assimilate the principles. Finally, the following chapters present other types of neural networks such as those based on competition and the very important backpropagation neural network.

The only thing that you can complain about is its high price tag. For anyone interested in the AI field, it is recommended.

Good, but not very mathematical.......2006-01-17

This is an excellent textbook for beginners, giving a clear picture of what neural networks are, and where they are used. It also talks about back-propagation, associative neural nets, and more. But the biggest flaw is that the book has little mathematics. And it also doesnt have any working code (only pseudo-code). So if you are considering buying this as a textbook for a NN course you are taking at your university, well, I would suggest you take a good look at the book at your library before you decide to buy it. Most university courses put neural nets on a firm mathematical footing and might also have course projects that have to be done by the student. This book can help you with neither of these. And the book's pretty expensive, I really wonder why.

Great Book.......2005-03-10

This is a great book on the topic. The author approaches the subject from a practial point of view. There are good examples on how to use NN with real world problems (i.e.: using perceptrons for character recognition). A good reference to have. However I would not recommend trying to code the algorithms yourself, but rather use a NN package .

The missing star is due to the book's price.......2002-10-31

I bought this book as a reference book during a graduate course I took in Neural Computation.

The book was clear and useful in presenting the topics, and more importantly, in presenting the algorithms in a clearn simple format which made it very easy to produce a computer program implementing these algorithms just by reading the book.

It was also useful by listing various things which have been done in literature to alter the algorithms for various purposes.

I suppose that people in the field of Neural Computation might find this book useful as an introduction book and also as a reference book (at least for its clear explenations and algorithms listings), but otherwise need more comprehensive books which cover a lot more math than this books does.

Actually, this is a good book for getting to know this dicipline for people who don't like to mess too much with calculus. Other books in this fiels contain more, a lot more, calculus in them, so I would also argue that this book is useful for people who want to understand the ideas, have a clear algorithm so its easy to implement, and at the same time, not worry about the math too much.

You don't see proofs here, at least not as much as I expected to, but I suppose that this follows the idea that this book is useful as a complementary book, rather than the authoritative book in the subject.

I only regret the high price of the book.

Great Book !.......2001-06-03

An objective book. The author explains with details each network model. Includes a detailed structured language of each algorithm, that makes easy to one write a computer program code. With the step to step numeric examples, the written program may be checked to acuracy.

Neural and Adaptive Systems: Fundamentals through Simulations

Average customer rating:

Excelente Libro
Mindstorms meets NN; same strengths and weaknesses
Okay book, not worth the price.
Very Helpful and Practical
The best book for adaptive signal processing

Neural and Adaptive Systems: Fundamentals through Simulations
José C. Principe , Neil R. Euliano , and W. Curt Lefebvre
Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

General | Computers & Internet | Subjects | Books

General | Engineering | Professional & Technical | Subjects | Books

General | Science | Subjects | Books

Networking | Computer Science & Information Systems | New & Used Textbooks | Stores | Books

Electrical & Electronics | Engineering | New & Used Textbooks | Stores | Books

General | Engineering | New & Used Textbooks | Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books

Look Inside Computer Books | Trip | Specialty Stores | Books

Look Inside Science Books | Trip | Specialty Stores | Books
Similar Items:

ASIN: 0471351679

Book Description

Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text
* The text and CD combine to become an interactive learning tool.
* Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations.
* Each key concept is followed by an interactive example.
* Over 200 fully functional simulations of adaptive systems are included.
* The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines.
* Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts.
The CD-ROM Contains:
* A complete, electronic version of the text in hypertext format
* NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator
* A tutorial on how to use NeuroSolutions
* Additional data files to use with the simulator
"An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight." -James Zeidler, University of California, San Diego

Customer Reviews:

Excelente Libro.......2007-06-03

Muy buen libro y lo mejor es que trae buenos ejemplos los cuales ayudan bastante en el entendimiento de las redes neuronales. Por supuesto es una lectura complementaria a textos más avanzados si es que se quiere ahondar en el tema.

Mindstorms meets NN; same strengths and weaknesses.......2005-09-02

I am quite conflicted in my thoughts on this book. The pluses are that it is comprehensive, thorough and comes with a useful pedagogical simulator. The minuses are that it is covers a vast range of subjects with a skimpy coat of mathematical and theoretical glue. When I found myself looking for more maths and theory, the text usually directed me towards the simulation. The simulations are good but the coding is invisible to the user due the range of NN widgets included (analogous to programming using the Lego Mindstorms visual programing tool). This is a downside for those hoping to learning coding techniques - there are not explored here - but will suit users that want a workbench to visualise concepts in the text. I confess that I became bored with the simulator about midway through the book.

The authors set themselves a formidable task in producing this book, and depending on the reader's needs thaey have either excelled themselves, or else have concocted a animaed dodo - interesting but near extinction.

Okay book, not worth the price........2004-02-26

The book covers the topics fairly well. Unfortunately, you won't ever use this book outside of a classroom. It seems like every page either has an illustration, or a box saying "run simulation 5.17 fom the CD, and here are 3 graphs from that simulation", or "Use the Excel plugin on the data from the CD". I suppose that is useful to those who can't follow the math. I believe it is also what causes the excessive cost of the book.

DON'T buy the book if you are looking for ways to include neural networks or machine learning into your existing applications. The book gives you formulas, and a workbench program that (once you figure it out) lets you customize it's own built-in formulas with its own data sets. It is not significantly useful when wanting to write your own adaptive system, nor does it have any significant code samples to learn from. You get the formulas and illustrations, figuring out how to apply them is up to you.

Very Helpful and Practical.......2001-09-23

Topics explored in this book include signal processing, feature extraction, linear and non-linear modelling, temporal models and an array of connectionist learning paradigms. These topics are demanding, particularly mathematically, however I have taken a lot from this book where I have had limited success with others. The authors know a great deal about their subject, which has enabled them to take a holistic approach, which explains fundamentals before building them into powerful solutions. Equations, while frequently cited are explained descriptively and often boxed separately from the main text. Learning is greatly enhanced by the 200 interactive tutorials. The NeuroSolutions software (limited version free) is the best I've seen in terms of the number of architectures and data processing algorithms provided and comes with an excel add in! I searched Amazon long and hard for this book and was not disappointed - it sets a new standard for technical education.

The best book for adaptive signal processing.......2000-03-30

This is the best book to learn adaptive signal processing. The authors teach you the spirit of adaptation thorugh simulation played by yourself. Moreover, readers can build their own system using the NeuralSolution. No matter you are a student or an engineer, this book is very useful.

Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition

Average customer rating:

Great book!

Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition
Sandhya Samarasinghe
Manufacturer: AUERBACH
ProductGroup: Book
Binding: Hardcover

Systems Analysis & Design | Computer Science | Computers & Internet | Subjects | Books

General | Programming | Computers & Internet | Subjects | Books

General | Certification Central | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

Look Inside Computer Books | Trip | Specialty Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 084933375X

Book Description

In response to an increasing demand for novel computing methods, Neural Networks for Applied Sciences and Engineering provides a simple but systematic introduction to neural networks applications. This book features case studies that use real data to demonstrate practical applications. It contains in-depth discussions of data and model validation issues along with uncertainty and sensitivity assessment of models as well as data dimensionality and methods to reduce dimensionality. It provides detailed coverage of neural network types for extracting nonlinear patterns in multi-dimensional scientific data in prediction, classification, clustering and forecasting with an extensive coverage on linear networks, multi-layer perceptron, self organization maps, and recurrent networks.

Customer Reviews:

Great book!.......2007-08-07

I found Dr. Samarasinghe's very easy to understand yet very comprehensive in its coverage of neural networks. The hand calculations really helped me see how the algorithms are applied to real-world problems. This is one of the best books on the subject that I own, and I own a bunch of them. I highly recommend it!

Fundamentals of Computational Neuroscience

Average customer rating:

Fundamentals of Computational Neuroscience
Thomas Trappenberg
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Paperback

Cognitive Psychology | Behavioral Sciences | Science | Subjects | Books

General | Behavioral Sciences | Science | Subjects | Books

General | Medicine | Subjects | Books

Look Inside Computer Books | Trip | Specialty Stores | Books

Look Inside Science Books | Trip | Specialty Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Medicine | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books
Similar Items:

ASIN: 0198515839

Book Description

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. Fundamentals of Computational Neuroscience is one of the first introductory books on this topic. 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 is aimed at those within the brain and cognitive sciences, from graduate level and upwards.

Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

Average customer rating:

Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Madan M. Gupta , Liang Jin , and Noriyasu Homma
Manufacturer: Wiley-IEEE Press
ProductGroup: Book
Binding: Hardcover

General | Certification Central | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

General | Software | Computers & Internet | Subjects | Books

General | Engineering | Professional & Technical | Subjects | Books

All Amazon Upgrade | Amazon Upgrade | Stores | Books

Computers & Internet | Amazon Upgrade | Stores | Books

Engineering | Amazon Upgrade | Stores | Books

Professional & Technical | Amazon Upgrade | Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 0471219487

Book Description

Provides comprehensive treatment of the theory of both static and dynamic neural networks.
* Theoretical concepts are illustrated by reference to practical examples Includes end-of-chapter exercises and end-of-chapter exercises.
*An Instructor Support FTP site is available from the Wiley editorial department.

Download Description

Fundamentals of Artificial Neural Networks

Average customer rating:

TDNN
Math Fundamentals of Neural Nets.
A Unified Theory of Neural Networks

Fundamentals of Artificial Neural Networks
Mohamad H. Hassoun
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

General | Certification Central | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

Networking | Computer Science & Information Systems | New & Used Textbooks | Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Look Inside Computer Books | Trip | Specialty Stores | Books
ASIN: 026208239X

Amazon.com

This book uses tools from nonlinear systems theory to provide a comprehensive foundation for the theory of neural networks. The emphasis is on computational capabilities and learning abilities of neural networks. The unified perspective of nonlinear systems leads to a clear understanding of various architectures and learning methods, and the two chapters on learning provide valuable insight. In addition to the most common feed-forward networks, the book analyzes radial basis function networks, classifier networks, clustering networks, and various models of associative memory. The book is intended to be used for a first-year graduate course. The required background includes basic topics in mathematics, such as probability and statistics, differential equations, linear algebra, multivariate calculus, as well as some knowledge of state systems, Boolean algebra, and switching theory.

Book Description

As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers.

Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references.

Proceeding in a clear and logical fashion, the first two chapters present the basic building blocks and concepts of artificial neural networks and analyze the computational capabilities of the basic network architectures involved. Supervised, reinforcement, and unsupervised learning rules in simple nets are brought together in a common framework in chapter three. The convergence and solution properties of these learning rules are then treated mathematically in chapter four, using the "average learning equation" analysis approach. This organization of material makes it natural to switch into learning multilayer nets using backprop and its variants, described in chapter five. Chapter six covers most of the major neural network paradigms, while associative memories and energy minimizing nets are given detailed coverage in the next chapter. The final chapter takes up Boltzmann machines and Boltzmann learning along with other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms.

Customer Reviews:

TDNN.......2002-03-13

I feel it is a very good book over-all for Neural Networks. It is one of the very few books that I came across with an excellent description of Time Delay Neural Networks (TDNN) and the associated learning algorithms.

Math Fundamentals of Neural Nets........2000-02-28

Prof. Hassoum's book is very good to introduce the reader in the mathematics of Artificial Neural Nets (ANN), including an interesting item explaining how to integrate Genetic Algorithms (GA) with Artificial Neural Networks (ANN) not found in this kind of work. Nevertheless, this is not a book for computing professionals because its necessary one to have a solid background on math to understand the ANN concepts along the chapters. Well written for mathematicians, it lacks pratical examples for better understanding the concepts explained in the book.

A Unified Theory of Neural Networks.......2000-02-16

Prof. Hassoun's book is almost the most complete book that builds a clear and broad foundation of neural networks. His unified approach to cast the problems of neural networks in a mathematical optimization models is excellent. The book is full of challenging and drill-like problems. The references cited blasts the door before the reader's eyes to explore worlds of applications. Prof. Hassoun's contribution to the field of Neural Networks is remarkable. After more than three years of taking two graduate courses using this book (and being lectured by Prof. Hassoun), I can hardly forget any detail. A excellent book which ideas get inscribed in your head. In a few word, The Bible of Neural Networks ...

Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications (Chapman & Hall/Crc Computer and Information Sciences)

Average customer rating:

Great book

Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications (Chapman & Hall/Crc Computer and Information Sciences)
Leandro Nunes de Castro
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

General | Programming | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books
Similar Items:

ASIN: 1584886439

Book Description

Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniques and important applications of nature-based computing. This book presents theoretical and philosophical discussions, pseudocodes for algorithms, and computing paradigms that illustrate how computational techniques can be used to solve complex problems, simulate nature, explain natural phenomena, and possibly allow the development of new computing technologies. The author features a consistent and approachable, textbook-style format that includes lucid figures, tables, real-world examples, and different types of exercises that complement the concepts while encouraging readers to apply the computational tools in each chapter. Building progressively upon core concepts of nature-inspired techniques, the topics include evolutionary computing, neurocomputing, swarm intelligence, immunocomputing, fractal geometry, artificial life, quantum computing, and DNA computing. Fundamentals of Natural Computing is a self-contained introduction and a practical guide to nature-based computational approaches that will find numerous applications in a variety of growing fields including engineering, computer science, biological modeling, and bioinformatics.

Customer Reviews:

Great book.......2007-02-18

I have to study about data mining and the professor recommended this book. After reading it, I think it's a great one. No wonder why the professor likes it.

Artificial Neural Networks for Civil Engineers: Fundamentals and Applications

Average customer rating:

Artificial Neural Networks for Civil Engineers: Fundamentals and Applications

Manufacturer: American Society of Civil Engineers
ProductGroup: Book
Binding: Paperback

General | Certification Central | Computers & Internet | Subjects | Books

General | Engineering | Professional & Technical | Subjects | Books

General | Science | Subjects | Books

General | Arts & Photography | Subjects | Books

Look Inside Computer Books | Trip | Specialty Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 0784402256

Principles of Neurocomputing for Science and Engineering

Average customer rating:

For beginners and graduate students
For beginners and graduate students

Principles of Neurocomputing for Science and Engineering
Fredric M. Ham , and Ivica Kostanic
Manufacturer: McGraw-Hill Science/Engineering/Math
ProductGroup: Book
Binding: Hardcover

General | Computer Science | Computers & Internet | Subjects | Books

General | Software | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

General | Arts & Photography | Subjects | Books

Electrical & Electronics | Engineering | New & Used Textbooks | Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Arts & Photography | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books

Look Inside Computer Books | Trip | Specialty Stores | Books
ASIN: 0070259666

Book Description

* Unlike other neural network books, this is written specifically for scientists and engineers who want to apply neural networks to solve complex problems
* For each neurocomputing concept, a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm
* Incorporates many detailed examples and an extensive set of end-of-chapter problems

Customer Reviews:

For beginners and graduate students.......2001-01-05

I strongly recommend this text for beginners and graduate students who want to understand Neural Networks. First, the text book explains the why, where, and how to apply Neural Networks, which is the most important point for understanding Neural Networks. Second, mathematical proofs, clear description of algorithms and MATLAB codes encourage me to do it myself. And a lot of mathematical foundation in the Appendix allow me to understand quite easily complex mathematical concepts.

For beginners and graduate students.......2001-01-05

Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience)

Average customer rating:

Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience)

Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

General | Certification Central | Computers & Internet | Subjects | Books

Neuropsychology | Psychology & Counseling | Health, Mind & Body | Subjects | Books

General | Psychology & Counseling | Health, Mind & Body | Subjects | Books

Cognitive | Psychology & Counseling | Health, Mind & Body | Subjects | Books

General | Mental Health | Health, Mind & Body | Subjects | Books

Physiology | Basic Science | Medicine | Subjects | Books

General | Medicine | Subjects | Books

Cognitive Psychology | Behavioral Sciences | Science | Subjects | Books

General | Behavioral Sciences | Science | Subjects | Books

Anatomy | Biological Sciences | Science | Subjects | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Medicine | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 0262161753

Book Description

Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics.

The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease.

Contributors: J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble.

Books:

Books Index

Books Home

Recommended Books