Neural Networks Books

Neural networks have seen an explosion in interest and application in the last 10 to 15 years, when they evolved from research on artificial intelligence One can find books on diverse subjects such as finance, medicine, and any physical or theoretical science with significant sections devoted to the use of neural networks in that discipline. I conducted a non-scientific survey (in 1 minute or less) of the importance of the subject matter by asking Amazon.com how many books it listed on subjects I thought might be equal in timing and importance. The results (below) imply a significant interest in neural networks, from readers and authors alike. My list does not report on the number of books that contain chapters or significant sections on neural networks.

·Neural Networks=1021 books listed; DNA=948 books; Enzymes=779 books, Genome=232 books, and Human Genome=100 books

Optimization Techniques is the second in a seven (7) volume series from Academic Press on neural network systems techniques and applications. The series presents itself as the first all-inclusive treatment of the subject matter and is aimed at a wide array of potential readers: researchers, students and practitioners in industrial, mechanical, electrical, manufacturing and computer engineering. As such, one would expect the series to be appealing to a more select audience of research workers focused on creating and improving neural networks, and not so much to those of us who use the applications and interpret the output. This seems to be the case.

This Volume in the series, claiming to be the first comprehensive treatment of optimization techniques including system structure and computational methods, presents the work of nineteen (19) contributors as a synthesis of what is known about neural networks and optimization techniques at the present time. The book is divided into ten (10) sections, each addressing different topic areas. I would not suspect that more than one or two sections would be of interest to the reader in an applied research field.

I found the sections on the learning of nonstationary processes and neural techniques for data analysis to be informative and well written. I did not anticipate having a warm feeling of confidence in my level of understanding the first time I read these sections. I am confident, however, that I know which direction current and future research will take on neural networks.

This book is an excellent book on the application of Neural networks to the financial world. This is a collection of articles for the knowledgeable in the area of finacial investments and Neural Networks. With this in mind this book constitutes an excellent reference book to any graduate course in any finance dept to apply intelligent decision making to financial estimates and investments. It is a reference book for all those out there who are thinking about doing research in this area. Both authors have written lots of publications in the area and that is a plus. Even though this book is getting to be little bit old, a new book coming out by the by the same first author and the same company seems to be an updated review on the subject and promises to become a reference too. Excellent refence!

The book is a measure of how neural networks have moved from a lot of hype into being a practical research technique. Zupan carefully explains the key concepts behind single and multilayer networks to an audience that he knows will not be familiar with most of the ideas. The level of maths is not trivial, but should be understandable to any chemist who has ploughed through several courses on quantum theory.

The book applies the networks to designing drugs. Given the inherent black box nature of the nonlinear feedbacks during the training steps, the book's detailed explanations should be reassuring to those using networks for the first time.

...This book gives an excellent view of the concept of ANN but covers them in pure mathemetical way. You can learn so much about the equations used to describe ANNs but I would doubt that you can learn enough to make you able to utilize them. It is a good read, but needs more practical approach to it, but talking about such a topic is not a well-defined path, so I give it a 4-star rating

This book opens up new possibilities. It includes a basically simple Graphical User Interface (GUI) that can be used in Microsoft Windows (and in fact uses it for the examples). I rate it with 4 stars instead of 5, because the instructions in the examples are for those experienced with NEURON. For beginners like myself, it would help to say which buttons should be clicked and which keys pressed.

This book describes the NEURON simulation system, which can be accessed for installation and instructions at the NEURON web site. Simulation implies using the realistic Hodgkin-Huxley neuron. NEURON was initially for individual neurons, but it has now been extended to networks.

For those who believe in the classical physical science of the 19th century, including physics, chemistry, thermodynamics, and the differential equations in which they are expressed, NEURON has a special meaning. The Hodgkin-Huxley neuron extended classical physical science to a wide range of neuron types and species. The reductionist work of Eric Kandel explained many types of synapses at the molecular level, and therefore explains the connection of neurons in a network in terms of classical physical science.

Our special interest is in networks of interneurons. The most accessible mammalian networks are those in the olfactory bulb of the rat. For this special class, classical physical science, using NEURON, extends into neurobiology. It DEFINES a physically possible network structure. It is likely that evolution will have exploited at least part of this structure to extend order. This possibility is there. It is real. And it is begging for study.

This work will not require a supercomputer. From the deterministic point of view of classical physical science, there is no magic in statistically large numbers of cells. Two dozen or less should be enough to display emerging order.

Freidemann Pulvermueller's "Neuroscience of Language" is an indispensible reference for anyone seeking more than cocktail-party knowledge about psychology, neurology, linguistics, and computational systems. Perhaps because it attempts to unite so many previously-unconnected fields in a concrete neural model of natural language (instead of in yet another abstract, mathematical model), the many technicalities of these fields unfold in a transparent, easy-to-follow order. The subtitle of Pulvermueller's book is not "Neuroscience for Dummies", but it succeeds in providing an integrated,lucid, instructive tour of the past 50 years of research in cognitive science.

Unfortunately, the book's subtitle is "On brain circuits of words and serial order", and when Pulvermueller finally turns to serial order in Chapter 8, I think his brain circuits start to malfunction. I say this because of an influential paper Karl Lashley wrote back in 1951, entitled, "On the problem of serial order in behavior". Briefly, the problem is that serial order isn't always serial (Lashley cited Spooner's toast "To our queer, old dean"). Unlike generations of pop pyschologists, Lashley's student, Noam Chomsky, understood this to be a devastating critique of behaviorism. The point about Spoonerisms is *not* that they are freaks of nature, but that they are ubiquitous in human behavior. Chomsky's prime example was the ubiquitous "transformation" of e.g. "John kissed Mary" into the passive voice "Mary was kissed by John", but almost every serial behavior in your human repertoire, from your route to work in the morning to the arpeggios you play on the piano, exhibits the serial non-seriality of Spoonerisms (or, more formally "metathesis"). Pulvermueller cites Lashley's paper, but he never really addresses metathesis. Instead, he builds his neural model out of "synfire chains", so that in the end the reader finds she has been given a superb 50-year tour of psycholinguistics only to wind up back in 1950, analyzing behavior in terms of neo-Skinnerian stimulus-response chains.

If you are interested in these issues, you should definitely get Pulvermueller's book, if only to understand how cognitive science became locked in this vicious circle of reasoning. To understand Spoonerisms, however, you should look at Loritz' "How the Brain Evolved Language". Unfortunately, Loritz follows the work of Stephen Grossberg, and that requires a really different way of thinking about thought--sort of like the difference between thinking in Newtonian terms and thinking in terms of relativity. I didn't really *get* Loritz (much less Grossberg) until I first read Jeff Hawkins' "On Intelligence". You might want to go this route, too.

Don't be mislead into imagining that just because the book is over 10 years old, that it is obsolete. While newer methods have emerged, the algorithms in the book are still worthy of study by anyone doing research in image analysis or neural nets.

While parts of the book might be too advanced for some readers, it presents a good summary of useful ideas that you can code. Or perhaps start from, if you're doing research.

The book is mostly a first course in numerical methods. Where the authors have provided code examples in C++ and Java. The methods in each topic would typically be found in several other numerical texts. On this basis, the main attraction of the book seems to be the example code.

Readers experienced in C++ or Java coding should be able to write code from scratch to implement methods.

This books focus on parallel aspects of simulated annealing, espically in term of multiple triels, the book gives both a mathematical provement and experimental results. The conclusion in this book is cited a lot in another famous book, 'Image Analysis, random fields and dynamic monte carlo methods' by Gernard Winkler.

When you have data that is present in some n-dimensional space, you often want to make clusters. The problem is that most methods have a subjective component. What is a cluster is sometimes a matter of definition, within a given method. Clusters can also be used to try to draw up regions of that n-dimensional space. This constitutes a classification of future data. Well, how to do so?

Abe explains an idea that has gained recognition recently. The concept of support vector machines. The label is perhaps a little clumsy. But Abe's book gives a good geometric understanding of current classification ideas and their limitations. And how these can be overcome using support vector machines.

Several variants are explored. Along with a tie-in to neural networks for training. The computations can be intensive for real data. But these days, that is less and less of a limitation.