Information Theory, Inference & Learning Algorithms

Book Description

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Customer Reviews:

Outstanding book, especially for statisticians.......2007-10-02

I find it interesting that most of the people reviewing this book seem to be reviewing it as they would any other information theory textbook. Such a review, whether positive or critical, could not hope to give a complete picture of what this text actually is. There are many books on information theory, but what makes this book unique (and in my opinion what makes it so outstanding) is the way it integrates information theory with statistical inference. The book covers topics including coding theory, Bayesian inference, and neural networks, but it treats them all as different pieces of a unified puzzle, focusing more on the connections between these areas, and the philosophical implications of these connections, and less on delving into depth in one area or another.

This is a learning text, clearly meant to be read and understood. The presentation of topics is greatly expanded and includes much discussion, and although the book is dense, it is rarely concise. The exercises are absolutely essential to understanding the text. Although the author has made some effort to make certain chapters or topics independent, I think that this is one book for which it is best to more or less work straight through. For this reason and others, this book does not make a very good reference: occasionally nonstandard notation or terminology is used.

The biggest strength of this text, in my opinion, is on a philosophical level. It is my opinion, and in my opinion it is a great shame, that the vast majority of statistical theory and practice is highly arbitrary. This book will provide some tools to (at least in some cases) anchor your thinking to something less arbitrary. It's ironic that much of this is done within the Bayesian paradigm, something often viewed (and criticized) as being more arbitrary, not less so. But MacKay's way of thinking is highly compelling. This is a book that will not just teach you subjects and techniques, but will shape the way you think. It is one of the rare books that is able to teach how, why, and when certain techniques are applicable. It prepares one to "think outside the box".

I would recommend this book to anyone studying any of the topics covered by this book, including information theory, coding theory, statistical inference, or neural networks. This book is especially indispensable to a statistician, as there is no other book that I have found that covers information theory with an eye towards its application in statistical inference so well. This book is outstanding for self-study; it would also make a good textbook for a course, provided the course followed the development of the textbook very closely.

Great wish it had more n option inverse problems.......2007-07-16

This is fantastic book. Really takes an intuitive approach to the material. The explanation of occam's razor is worth the price of the whole book. Highly recommended.

Great Book As Far As It Goes.......2006-03-27

I have used this to get a good background in the topics covered, especially inference theory, and in general I found it to be great book which fills a market gap. The only sins I see are sins of omission. I personally would have enjoyed seeing a more task driven organization. I seem to need these methods periodically but I never seem to need the same method twice. Also, many of the techniques are heavily iterative, i.e., monte carlo, neural networks, etc. This is fine but much of what I do is in the context of simulations where 100,000 step iterative methods don't work so well because of resource constraints. Historically, that has been the problem with many of these methods. They are useful for relatively small domains but don't necessarily work that well for "real" problems. That is probably why more task oriented books are not available. Of course the author is following the outline of the current research into the subject manner which in turn is largely determined by "interesting" and "doable" problems. The real progess in this field will come when the problems are formulated more by what is needed in the nontraditional domains of application. A good example of a useful compression (and identification in some cases) technique that is not covered is Principal Component Analysis. Technically, it is in none of the technique domains covered in this book, but it would have been nice to see some of the methods in the book compared with PCA. The author does make the statement at one point that image recognition is an interesting problem for which the method being discussed at the time is used. Nevertheless, this is a great overview of the subject manner and is very entertaining. That in the long run probably explains the problem: it is a textbook.

A must have..........2005-03-01

Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.

This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

Good value text on a spread of interesting and useful topics.......2005-02-20

I am a PhD student in computer science. Over the last year and a half this book has been invaluable (and parts of it a fun diversion).

For a course I help teach, the intoductions to probability theory and information theory save a lot of work. They are accessible to students with a variety of backgrounds (they understand them and can read them online). They also lead directly into interesting problems.

While I am not directly studying data compression or error correcting codes, I found these sections compelling. Incredibly clear exposition; exciting challenges. How can we ever be certain of our data after bouncing it across the world and storing it on error-prone media (things I do every day)? How can we do it without >60 hard-disks sitting in our computer? The mathematics uses very clear notation --- functions are sketched when introduced, theorems are presented alongside pictures and explanations of what's really going on.

I should note that a small number (roughly 4 or 5 out of 50) of the chapters on advanced topics are much more terse than the majority of the book. They might not be of interest to all readers, but if they are, they are probably more friendly than finding a journal paper on the same topic.

Most importantly for me, the book is a valuable reference for Bayesian methods, on which MacKay is an authority. Sections IV and V brought me up to speed with several advanced topics I need for my research.

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Average customer rating:

Good book for computational neuroscience
"Theoretical Neuroscience" Dry but Informative
Good starting point for undergraduate students
Theoretical Neurosciences from a Computational Perspective
Great textbook and reference

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Peter Dayan
Manufacturer: MIT Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0262541858

Book Description

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.

The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

Customer Reviews:

Good book for computational neuroscience.......2007-01-28

I am a mathematician and economist interested in how human brain works. To me, (so far) this is the best book using equations to describe the overall picture of brain functions. Even though it might not touch in-depth research topics, I am sure it gives anyone interested in neuroscience very solid foundations on which more advance topics are built. (It actually invites me to more in-depth research topics, such as reinforcement learning, reward-punishment system, etc.)

If math is your familiar language (says, system of differential equations and Bayesian probability), and you are interested to know, in technical details, how the brain functions, this book is for you. Then, I think, you can go into research topics of your interests after finishing reading this book.

"Theoretical Neuroscience" Dry but Informative.......2006-03-23

"Theoretical Neuroscience" is an in-depth introduction to modeling of neural systems from the chemical/electrical processes within neurons, up through small networks of neurons. It is a little dry, but provides a wealth of information on modeling the electrophysical and computational properties of neurons.

Good starting point for undergraduate students.......2005-07-05

This book covers a wide range of different and important subjects of this field and provides by this a good overview to students new in neuroscience. On the other hand side, the topics discussed are not described thoroughly, but stay on the surface. This maybe no big problem for undergraduates who try just to understand the basics but certainly this is not satisfactory for more advanced students or researches.

In my opinion, this book blurs the view of the reader by presenting results about experiments and theoretical models side by side in a way that no fair and solid discussion is provided indicating clearly the limitations and problems of current models. By this, one could get the feeling that the presented models are more than tool to analyse data. However, exactly this is not true for most of the models as can be seen by the fact that these models can also be found in other areas than neuroscience with other interpretations.

Theoretical Neurosciences from a Computational Perspective.......2004-06-11

This text will become a standard course book for Graduate Schools in Computational Neurosciences. You need to know advanced engineering mathematics & probability theory to be able to understand this book. Dayan & Abbott model primary visual cortical, MT, LIP, and Motor cortical neurons as single units, but also as populations (clusters) of firing cells. They discuss Bayes Theorem, probability theory as it applies to the brain, and parietal lobe function as well. They derive all the equations associated with these models for the student so that more advanced parts of the book are comprehensible. The book is not meant to be a general Neuroscience book, but rather a course book about neuronal modeling, computational neurobiology, and neural engineering. It serves these three purposes well. In my opinion, this is the best written account of neuron modeling out there for the graduate student and researcher. Methods in Neuronal Modeling by Christof Koch is the other great book on this subject. If you own these two books you should be able to advance in high level neural modelling. There are numerous equations and formulae of interest throughout each chapter in these two volumes. The price of 39.00 USD for the hardcover is really quite a bargain.

Great textbook and reference.......2003-08-16

This book is certainly the most thorough textbook currently available
on many aspects of computational neuroscience. It works very carefully
through the fundamental assumptions and equations underlying large
tracts of contemporary quantitative analysis in neuroscience. It is
an ideal introductory book for those with a quantitative background,
and is destined to become a standard course book in the field.

Adaptive Modelling, Estimation and Fusion from Data

Average customer rating:

Where is the data ?

Adaptive Modelling, Estimation and Fusion from Data
Chris Harris , Xia Hong , and Qiang Gan
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540426868

Book Description

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input.

This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency.

Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Customer Reviews:

Where is the data ?.......2003-05-26

While i do not deny the contribution at providing a more or less complete treatment of neurofuzzy techniques for data modeling, i'm still wondering where the non-theoretical chapters dealing with how to cope with real data with these techniques are hidden. The few examples in the book are mostly artificial and very limited in their scope. i'm not sure people dealing with data have an interest at reading this book, it's more about neurofuzzy techniques than data modelling.

The book edited by Schwefel, Weneger and Weinert entitled "Advances in computational intelligence" published by Springer emphasizes a lot more on fuzzy techniques based on real data although it does not discuss the "neuro" part of "neuro-fuzzy" techniques.

Book Description

Rethinking Innateness asks the question, "What does it really mean to say that a behavior is innate?" The authors describe a new framework in which interactions, occurring at all levels, give rise to emergent forms and behaviors. These outcomes often may be highly constrained and universal, yet are not themselves directly contained in the genes in any domain-specific way.

One of the key contributions of Rethinking Innateness is a taxonomy of ways in which a behavior can be innate. These include constraints at the level of representation, architecture, and timing; typically, behaviors arise through the interaction of constraints at several of these levels. The ideas are explored through dynamic models inspired by a new kind of "developmental connectionism," a marriage of connectionist models and developmental neurobiology, forming a new theoretical framework for the study of behavioral development.

Customer Reviews:

The most important science book I've read in years.......2006-12-31

I came across this book while studying applied linguistics, and was curious about what Connectionist theory had to say against Noam Chomsky's nativist approach, which is that language is too complex a thing to be learned in the same way that we learn to swim or drive a car, and that it must be genetically ingrained, with a specific "language gene" that determines the fundamental parameters of how all human languages work.

Until reading this, I had heard all the conventional arguments against connectionsim -it was all computers and had nothing to do with the human mind, no computer simulation could ever come close to mimicing the complexity of the human mind, etc.

This book, mostly concerned with human development, has some fascinating and paradigm-changing ideas to add to the debate. If genes are so important, the authors argue, why don't we come out of the womb as fully formed adults with everything we need to know hardwired into us, as some lower species are? The authors show that there are simple flowers that have more genes than we do, demonstrating that gene count isn't the last word on an organism's complexity.

The authors make a powerful case that the state of childhood , and the complex development our minds experience during this time, is the reason that genes with specific codings don't have to do all the work- we are formed in interaction with our environments.

Rather than explaining everything, connectionist models simply demonstrate how, on the simplest level, our minds COULD work. While the models are simple, the results are fascinating. While obviously far less complex, the models really do demonstrate some of the quirks of human learning and acquisition in ways that more rigid, rule-based artificial intelligence doesn't.

I could write more, but this is a sprawling book packed with countless ideas, and even a brief summary would cover several pages. I admit that it can get technical at times, and I had to limit my reading of it to a few pages a day to fully digest it. But if you want to learn about this subject and have the dedication to get through it, it's an extremely worthwhile and rewarding investment of your time.

Very Technical.......2006-11-02

This book contains some thoughtful reasons for believing that many evolutionary psychologists overestimate how much information about the human mind is encoded in genes. However, it is mixed in with some highly technical developmental neurobiology that only a few specialists are likely to find interesting.
For nonspecialists, David Buller's book Adapting Minds says similar things about innateness in a style that is more suited for laymen.

Chomsky is dead or at least dying..........1999-10-18

Grammar isn't encoded in our genomes. It is learned. The beginnings of the proof are here. This is an important book. Read it and it's companion: Exercises in Rethinking Innateness : A Handbook for Connectionist Simulations; which gives you hands on with the models discussed.

Neural Network Modeling using SAS Enterprise Miner

Average customer rating:

Can still be useful
Great Book

Neural Network Modeling using SAS Enterprise Miner
Randall Matignon
Manufacturer: AuthorHouse
ProductGroup: Book
Binding: Paperback

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ASIN: 1418423416

Book Description

This book is designed in making statisticians, researchers, and programmers aware of the awesome new product now available in SAS called Enterprise Miner. The book will also make readers get familiar with the neural network forecasting methodology in statistics. One of the goals to this book is making the powerful new SAS module called Enterprise Miner easy for you to use with step-by-step instructions in creating a Enterprise Miner process flow diagram in preparation to data-mining analysis and neural network forecast modeling. Topics discussed in this book An overview to traditional regression modeling. An overview to neural network modeling. Numerical examples of various neural network designs and optimization techniques. An overview to the powerful SAS product called Enterprise Miner. An overview to the SAS neural network modeling procedure called PROC NEURAL. Designing a SAS Enterprise Miner process flow diagram to perform neural network forecast modeling and traditional regression modeling with an explanation to the various configuration settings to the Enterprise Miner nodes used in the analysis. Comparing neural network forecast modeling estimates with traditional modeling estimates based on various examples from SAS manuals and literature with an added overview to the various modeling designs and a brief explanation to the SAS modeling procedures, option statements, and corresponding SAS output listings.

Customer Reviews:

Can still be useful.......2007-08-16

This book gives an introduction in how to implement neural networks using SAS Enterprise Miner, and is written for those who already have a basic understanding of them. Neural networks are straightforward to understand from a mathematical standpoint but their use in real applications can be awkward, especially if they are required to work in out-of-sample contexts. Enterprise Miner will not alleviate these difficulties, but it does offer a more straightforward way to build the neural network architectures, due to its menu-driven approach. The book is somewhat out-of-date, since it is written for those readers who are using Enterprise Miner 4.3, but most of the book is still relevant for those who are now using SAS Enterprise Miner 5.2. The latter is JAVA-based, and has some additional capabilities that one cannot find in Miner 4.3. Readers who will not be using the new features in Miner 5.2 will therefore find the book useful. The author also discusses some of the foundational aspects of neural networks, and how they compare with other methods for doing prediction and classification. Of course if one has access to Enterprise Miner 5.2, the accompanying documentation will lessen the need for this book.

Great Book.......2006-09-12

I just purchased this book, and I must say that I am truly impressed. When I picked this book up at the mailbox, I thought there must be a mistake, there must be two books here. There wasn't, this book is an inch and a half thick by itself, and filled with goodies form the first to last page. This book would be perfect to teach a data mining class on Enterprise Miner and Neural Networks.

We used the neural procedure in UCF's Data Mining 2 class and SAS does not provide any support. This book is at the cutting edge of using the Neural procedure in open code.

The book provides the syntax and statements for using the Neural prodecure and the DMDB procedure. SAS does not support these procedures. If you ever want to write a macro using a neural network you will want to use the Neural procedure in open code. The author also provides numerous code examples with different architectures. He also does a good job of explaining how neural networks get stuck in local minimums, and of explaining the link procedures and what a miner would have to do to score a validation/test data set.

The book also explains the nodes in Enterprise Miner and also guides the miner through building a diagram. If only the author can write a book about decision trees and the Split procedure.

Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience)

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Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience)
Chris Eliasmith , and Charles H. Anderson
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0262550601

Book Description

For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.

The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.

Exercises in Rethinking Innateness: A Handbook for Connectionist Simulations (Neural Network Modeling and Connectionism)

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A Great Introduction to Connectionism
A Great Introduction to Connectionism

Exercises in Rethinking Innateness: A Handbook for Connectionist Simulations (Neural Network Modeling and Connectionism)
Kim Plunkett , and Jeffrey L. Elman
Manufacturer: The MIT Press
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Binding: Paperback

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ASIN: 0262661055

Book Description

This book is the companion volume to Rethinking Innateness: A Connectionist Perspective on Development (The MIT Press, 1996), which proposed a new theoretical framework to answer the question "What does it mean to say that a behavior is innate?" The new work provides concrete illustrations--in the form of computer simulations--of properties of connectionist models that are particularly relevant to cognitive development. This enables the reader to pursue in depth some of the practical and empirical issues raised in the first book. The authors' larger goal is to demonstrate the usefulness of neural network modeling as a research methodology.

The book comes with a complete software package, including demonstration projects, for running neural network simulations on both Macintosh and Windows 95. It also contains a series of exercises in the use of the neural network simulator provided with the book. The software is also available to run on a variety of UNIX platforms.

Customer Reviews:

A Great Introduction to Connectionism.......2000-05-02

Here's a self-contained introduction to connectionist modeling. Easy to read and straight-forward, this text provides software and excercises aimed at stepping a novice through the basics of connectionism. Designed to accompany Rethinking Innateness (1996), these examples provide a glimpse into the world of cognitive modeling. The examples can, at times, be frustrating and the text is in need of more debugging hints; yet, the simulations are rewarding and thought-provoking. While those already familiar with connectionism will find the excercises too basic, those curious about connectionism will find the book a great place to start and one that doesn't bog the reader down with technical jargon. It is accessible, enjoyable, and written by two key players in connectionism: Kim Plunkett and Jeff Elman. Well worth reading, but only if the reader is willing to work through the basic simulations and answer the excercises along the way.

A Great Introduction to Connectionism.......2000-05-02

Learning From Data: Concepts, Theory and Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

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An up to date, unifying textbook on learning/modelling depen
Study in easy

Learning From Data: Concepts, Theory and Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Vladimier S. Cherkassy
Manufacturer: John Wiley & Sons
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Binding: Hardcover

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ASIN: 0471154938

Book Description

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples, Learning from Data:
* Relates statistical formulation with the latest methodologies used in artificial neural networks, fuzzy systems, and wavelets
* Features consistent terminology, chapter summaries, and practical research tips
* Emphasizes the conceptual framework provided by Statistical Learning Theory (VC-theory) rather than its commonly practiced mathematical aspects
* Provides a detailed description of the new learning methodology called Support Vector Machines (SVM)
This invaluable text/reference accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.

Customer Reviews:

An up to date, unifying textbook on learning/modelling depen.......2001-12-19

The material contained in the textbook presents and discusses recent developments, but also important statistical (learning theory) concepts such as model selection, regularisation etc, in a unifying manner.
Although the authors are somewhat biased towards kernel methods, support vector machines in particular, they discuss the applicability and performance of other methods (neural networks, fuzzy systems, etc.). This is to be commended, as there are not many books that discuss all such methods in a common framework.
This book is highly recommended to readers wishing to gain a good understanding of the most significant statistical and other methods being applied in industry, and continuously experiencing significant academic research. A set of very good references (some mandatory and well known in the research community) presented at the end of each chapter directs the reader to some very useful material and scientific publications. This is a book that will particularly appeal to the research/academic community.

Study in easy.......2000-08-20

This book is excellent and easy to study. Graduate students will find the book statistical learning theory and support vector machines(SVMs),especially learning system based on recent advances in machine learning and multiobjective optimization. This book describes the Vapnik and Chervonenkis(VC) theory's generalization abilities. For statisticians, Applied mathematician, mechanical engineers and most graduate student are interested in reading this book. This is a very good excellent reference!!

Modeling Brain Function: The World of Attractor Neural Networks

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Of historical importance

Modeling Brain Function: The World of Attractor Neural Networks
Daniel J. Amit
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

ASIN: 0521421241

Book Description

One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers.

Customer Reviews:

Of historical importance.......2005-10-04

The study of the physics of the brain from the standpoint of dynamical systems was very popular during the 1980's. The theory of chaotic dynamical systems, and the accompanying concepts of strange attractors, horseshoe maps, and fractal basins of attraction was the subject of intense research at that time. It was inevitable perhaps that these theories would be applied to the understanding of the brain, given the dynamical nature of the neuronal synapse. This book, published in 1989, gives a good overview of what was known at the time. It could be read by anyone with a background in dynamical systems and some elementary knowledge of brain biology. The mathematics is also straightforward in that the author does not bring in any of the heavy tools from differential topology or measure theory, which is normally done in discussions of dynamical systems.

There are some points made in the book that must be understood by the reader because the author feels that they are needed to build a successful model of the brain. For example, he discusses the notion of an `input system', which is a system that, for each input, produces and output with the same "status." Cognitive discrimination must be used at the input level, if one is to avoid the use of the `homunculus' (the little external observer), for distinguishing between "good" and "bad" outputs. The major task in the author's view is to produce "exceptional" input-output relations, i.e. relations that correspond to intuitions about cognitive processes. A successful brain model, i.e. one that is able to incorporate memory, should be able to distinguish between stimuli that are familiar from those that are to be submitted to the brain for processing or learning. Thus the model must avoid the use of what the author calls `spontaneous computations', which require an external observer (the homunculus again) to interpret the relation between the input and the output. The author gives an example of a system that performs only spontaneous computations early on in the book. Hence the author proposes the use of artificial neural networks (ANNs) to avoid the occurrence of spontaneous computations. An ANN organizes stimuli in association classes represented by an attractor, and all the stimuli in a particular class are associated with the attractor to which they flow. The author feels that ANNs are more adept at respecting the requirement that for mental computations, which are essentially operations on temporal sequences of data, some record of the initial input sequence must be carried along on a parallel channel, in order to provide the outcome with specific "meaning" and a correspondence to the assigned task.

These considerations on the dependence of the processing on the initial input motivate the author to discuss the role of ergodicity in the dynamics of the neural systems of the brain. As the author shows, any generic system subjected to noise will be ergodic, so that eventually the system will access each of its possible states in a manner that is completely independent of the initial state. The author points out two ways in which ergodicity can be avoided: one is to assume that the network is noiseless, and thus only certain moves are allowed from each vertex; the other is to assume that `cooperative phenomena' is present. Since the first possibility is rather exceptional, the author chooses the second, and gives detailed discussion on how cooperative behavior can arise in ANNs. One interesting, and ubiquitous example that he discusses for cooperativity as an emergent property is the Ising model. Mathematically, the breaking of ergodicity involves the taking of the thermodynamic limit, and a necessary condition for emergence is this context is the asymptotic degeneracy of the eigenvalues. To illustrate how this is done, the author uses the solution of a master equation that characterizes the probabilities of making transitions from one state to another in the system.

In order to build a credible model of the neuronal processes of the brain, the author is aware that such a model has to be able to deal with input in the form of temporal sequences, and not just single patterns. He devotes an entire chapter to this in the book, motivating his discussion with the notion of a `central pattern generator' (CPG). The simplicity of CPGs is a concern and the author is aware that such simplicity does not exist in models of cognitive processes. Nevertheless the modeling of CPGs using neural networks can add credence to the program to model general brain processes in terms of neural networks, complex as they can be.

One of course must be able to deal with both the storage and the retrieval of temporal sequences. After discussing some of the early research dealing with these needs, the author then reviews a strategy for dealing with temporal sequences that involves the notion of a `quasi-attractor', which is a network state that acts like an attractor for a short period of time. Quasi-attractors are used to delay the transfer of information out of the attractor. Thus the transitions are governed by synapses that have a time delay. The influence of a pre-synaptic neuron through these synapses will arrive later than the influence coming through a `stabilizing' synapse. The latter type of synapse arises because of the `stabilizing' term in the network model that ensures that if the network is in a state that is identical to a stored pattern then the network will remain there. The author shows how the network can use these delayed transitions to deal with temporal sequences in a manner that is acceptable, i.e. in a way that the `cognition time' is of the order of magnitude of the delay. The author discusses an example dealing with the counting of chimes, in order to give credence to his constructions. In this example it is seen that the network resides in each of the quasi-attractors for a long enough time so as to allow the output neurons to identify the cognitive event.

Nonlinear Dynamic Modeling of Physiological Systems (IEEE Press Series on Biomedical Engineering)

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The only well-writen up-to-date book for nonlinear modeling

Nonlinear Dynamic Modeling of Physiological Systems (IEEE Press Series on Biomedical Engineering)
Vasilis Z. Marmarelis
Manufacturer: Wiley-IEEE Press
ProductGroup: Book
Binding: Hardcover

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Identification of Nonlinear Physiological Systems (IEEE Press Series on Biomedical Engineering)

ASIN: 0471469602

Book Description

The study of nonlinearities in physiology has been hindered by the lack of effective ways to obtain nonlinear dynamic models from stimulus-response data in a practical context. A considerable body of knowledge has accumulated over the last thirty years in this area of research. This book summarizes that progress, and details the most recent methodologies that offer practical solutions to this daunting problem. Implementation and application are discussed, and examples are provided using both synthetic and actual experimental data.
This essential study of nonlinearities in physiology apprises researchers and students of the latest findings and techniques in the field.

Customer Reviews:

The only well-writen up-to-date book for nonlinear modeling.......2005-06-24

The book gives an introduction, as well as a more in-depth look on a number of parametric and nonparametric modeling approaches, as well as their applications to modern physiological system modeling, focusing on the nonlinearies and nonstationarities that need to be addressed during this approach. Although one must be equipped with sufficient understanding of signal processing and a strong mathematic background to access the field of nonlinear modeling, this book is as accessible as it can get, providing constantly examples and applications for each concept it introduces.

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