Neural Networks Books

Considering intense efforts in bio-inspired computing, taking the form of genetic algorithms, swarm intelligence, and artificial life, it is not surprising that the field would also gain inspiration from the workings of the brain, whether the brain comes from a human or some other mammal. This is both an exciting development and a difficult one, not only because a complete working model of the mammalian brain is not available yet, but also because of the sheer computational power needed. With faster machines of course, the second problem will be alleviated, and the first is undergoing rapid development, thanks to the research efforts in neuroscience. This book is a collection of articles that give a review of some of research in neuroscience-inspired computing. Since they are review articles, readers will not find in-depth discussion, but references are given that will assist curious readers who need more information on a particular topic.

The editors introduce the subject of neuroscience-inspired computing by pointing out some of the main sources for this inspiration. These sources serve to divide the book into four sections. The first of these concerns the modular organization of the brain. Even though the view of the human brain as being composed as specialized modules is still the subject of intense debate, there is experimental evidence from brain imaging studies that there are regions in the brain that are correlated with cognitive functions. Neural networks are of course used extensively in business and industry, but they are highly task specialized, and it therefore would be interesting, and useful, to entangle these networks together in order to enable the resulting system to solve more general tasks.

Another inspiration comes from the amazing robustness of the human brain. Through various recovery mechanisms, a damaged brain still is able to function to a large degree, and it therefore would be useful in emulate these mechanisms in non-biological machines. The editors discuss briefly various approaches that have been taken in the construction of models of both recovery through regeneration and via functional reallocation. Several papers in the book illustrate the construction of these models. One of these stands out with its emphasis on the creation of neural systems that are dynamic and adaptive. Everyone who has designed neural networks for practical use is aware of the fine-tuning needed to create a successful neural architecture. The authors of this particular article use a neuron development simulator along with evolutionary algorithms to evolve various neuron morphologies and architectures.

The third source of neuroscience-inspired computing comes from the neurophysiology of the brain. The performance of the brain is dependent on the temporal correlation between collections of neurons and brain regions. In the typical construction of a neural network, time dependences are usually not taken into account. This prohibits the neural network from dealing with data that is temporal in nature. In one of the articles in the book, the author describes the neuroscience behind time-dependent learning and proposes an associative learning rule that respects the potentiation or depression of the neuronal synapse at long time scales. The model of dynamical synapse that he proposes involves the computation of the excitatory post synaptic potential at the synapse and the backpropagating action potential. A learning rule is then constructed which depends on the cross-correlation between these two signals. This model, along with others that are discussed in other articles in the book, illustrate the role of timing and synchronization in neuronal processes. One of the more exotic models discussed in this regard is based on chaotic dynamics. Although the modeling of the brain as a collection of chaotic neural objects is difficult to validate because of the long time scales and large amount of data required, the model is discussed in sufficient detail to make it worth reading.

The last source of inspiration concerns the memory storage capabilities of the human brain. One of the articles in the book concerns the construction of artificial neural networks that can deal with sensitive to contexts, is hierarchical and extensible. The article is more ambitious than the others in the book as it discusses many difficult issues in the neuronal modeling. The goal of this modeling is to account for the ability of the human brain to engage in abstract reasoning. The local associationist algorithms that are usually used cannot emulate symbol-like behavior, and so the authors attempt to use recurrent nets and `schemata' to deal with this issue.

The articles definitely motivate the reader to investigate further the status of research in neuroscience-inspired computing. Further progress in neuroscience will be needed before machines can be constructed which emulate the brain in more detail. But even without a full understanding of the human brain machines could be constructed that use some approximate features of the human brain. These machines will have capabilities that may be better than those whose functioning or computational abilities are not based on human brain processes or its modular structure. The practical use of these machines will then motivate the construction of even better machines as more knowledge of brain processes becomes available.

The most impostant arguments on RNN are treated in this book. Expert scientist have wrote book's chapters. I'm interesting on the problem of vanishing gradient

Looking at the content of first 14 pages it seems that this book is going to be a very good reference for the researchers as well as beginers of the Evolutionary computing in Control. The conceptual part is also good as it can help beginers to get in to cognitive approach to the problem as tradinational methods are not useful in real time application and can now only used for comparision. Very Good Approach from the Editors.

It seems that most texts on fuzzy and nnets are in very basic form. Given the (fact) substantial use of both types of controllers at the most advanced levels of technology and finance it seems somewhat strange (trade secrets). If you already have Matlab's Fuzzy or NNets the material is somewhat redundant, given that the M-files are easier to work with in Matlab than using the Microsoft Word connection (more later). The manual allows the basic concepts to be easily transported to MathCad and for this reader is worth the book. It is for the begginer without Matlab that this text really shines. It has the Microsoft Words ability to work with the Matlab (although it is not installed) interface to show the reader how the two,fuzzy and nnets, work together. At the very least it allows someone to determine if they wish to proceed with topic (this is the future of control systems). I found that building a sub program in MathCad using set theory operators, to call the sub-routines of fuzzy and nnets worked best. This was a just for fun. Both Matlabs Fuzzy and nnet toolboxes work far better. This text adds fill in material for both Matlabs fuzzy and nnet toolbox texts.

The book is not a standalone book, textbook nor introduction to neural networks. Though someone with Neural Net experience and knowledge would make sense of it. It has to be used with the Neural Network software sold alongside it. As it says, it is a Manual and User guide.

It is thorough and encompassing, that is after a new and inexperienced user has mastered the intial usage of the software (which would take from three to six months of regular continuous use).

The range of examples and Neural network types is broad, useful and helpful, including the illustrations.

The problem it has, is that it has been written by someone, who produced and wrote the software (never the best idea). So there are a lot of items which are not clear to a beginner, i.e. someone who has just purchased the software. It also omits a number of operational facts, which are a problem, e.g. it does not tell you what the maximum path length is, and there is one; so using the software in this case causes errors without them being apparent. There are other similar technical omissions (which the programer would just take for granted...famous last words...."Well I knew!").

Though it has to be said, that the bulk of the instructions are clear and meticulous.

It also omits a "what if" section, e.g (to use an analogy) if the 'Creme Anglais' (custard) goes lumpy, what do I do?

Included is a section most often missing with other producers of software "Error Messages", in fact there are four sections on different types of messages. That is a very definite bonus, and serves as a good example of the meticulousness, that has been and can be used by the authors.

In marked contrast to the "Error Messages" sections, there is a Chapter on one of the additional tools supplied "Competitor", which has been written in a very unhelpful and inadequate manner, almost as an afterthought. As a result users end up having to sort out the use and methodology of what is in fact, a very useful tool, by trial and error.

Is it it worth $199? Only if you bought the software, and then you do not have much choice, you receive it anyway.

The attraction of this book is the chance of serendipity. The sheer joy and possibility of thumbing through it and stumbling across something germane to your research, but totally unforeseen by you or others.

The book sits astride several disciplines. Mostly biology. But also computer networks, of which, of course, the Internet is the primary and largest example. But the book also covers some portions of sociology. The classic six degrees of separation between any two people in the world. Actually this is more a metaphor than the literal truth. But still useful in understanding human networks.

If you are currently working with some type of network, your expertise in it, while being a strength, may also be a weakness if it makes you unaware of qualitatively different networks that yet have some commonality with yours.

Book covers a broad range of subjects in an attempt to put an overview on relationship between the various parts previewed by the table of contents. This it does very well. This book is good for someone who has just read about nnets or fuzzy in Vogue or Cosmopolitan and wants a little deeper info. However, it does little to explore, as an example the exact relationship between say an adaptive neural net and non-adaptive. Or why the cart-pole problem cannotbe stabilzed past 36 degrees with any system. The math contained can be transported to MathCad with some difficulty depending on the reader's ability. Matlab provides toolbox for both fuzzy and nnets, including all the topics covered in this book. In addition Segueno type linear output fuzzies are discussed and incorporated in their Simulink system.

This book has dealt with in depth the various entropy error measures that can be used for the neural networks. The Minimum entropy error measures and the Maximum entropy error functions like the Kapur measures, Sharma-Mittal error measures are discussed as an alternative to the classical square error methods when the input and the teacher values are stochastical variables. The book also has good introduction and is well written. The book has experimental data to support the claims made by the author.

The Mechanisms of Cortical Development describes current thinking about both the general principles and the specific stages of corticogenesis in an order that parallels their occurrence in development. The book begins by describing how cortical precursor cells in the ventricular zone know when to divide and when to migrate; where to migrate and where to stop; and what cell-type and which cortical layer to differentiate into. The book then discusses how the complex network of efferent and afferent connections between cortical and subcortical structures is formed, and the mechanisms that regulate naturally occurring cell death, which is an integral part of this process. The book finishes by considering the genesis of sensory `maps' and the receptive field properties of the sensory neurones therein. The text is clear and well written. The figures presented in each section are simple but informative. The general terms and principles used in the text are explicitly defined, and tables are provided that describe the many factors (e.g., trophic, extracellular matrix, and gene transcription factors) that have been implicated in cortical development - information that in of itself makes this book significant among neuroscience texts.

While it is clear from reading The Mechanisms of Cortical Development that much progress over the last few decades has been made towards understanding how the cortex forms, including the cellular, chemical and molecular processes involved, we may never completely understand how these different processes come together to create the complexity of the cortex. Where there is still little understanding, Price and Willshaw also consider the questions researchers in the field are currently studying, the hypotheses that have been proposed as answers to them, and an analyses of the techniques that are being used to test these hypotheses.

In short, this book should prove eminently useful to both specialists and students of neuroscience alike, especially given its unique perspective, which, reflecting the expertise of the authors, includes both biology and biological modeling. Instead of making the text too diverse, rather this dual perspective facilitates a more thorough consideration of the complex processes by which the cortex forms.

(From the British Neuroscience Association Newsletter)

I read this book as part of a Robotics Research project. It is a good introduction to Neuronal Modeling, and it was the first technical handbook of computational neuroscience. The book is a series of 13 articles on topics such as computer simulations of neural circuits, biophysical mechanisms for computation in neurons, etc. Each chapter concludes with a description of the model discussed and the details of its implementation on the computer. Since it is a series of articles, with many authors, the book feels a little bit fragmented. However, it is put together nicely and must have been skillfully edited. The book is easy to read, as well as interesting.

The book should be of interest to a variety of people in Medicine and Technology (other than the people in the specific field), but especially to those who work with Artificial Neural Networks. An interested layman could also read this book. I have to admit that I have not read the second edition of this book, but hopefully it is equally good, in addition to being more up to date, so the second edition would probably be the one you should buy first.