Introduction to the Theory of Neural Computation (Santa Fe Institute Studies in the Sciences of Complexity)

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Clear and logical exposition

Introduction to the Theory of Neural Computation

A Broad Survey

Introduction to the Theory of Neural Computation (Santa Fe Institute Studies in the Sciences of Complexity)
John A. Hertz , Richard G. Palmer , and Anders Krogh
Manufacturer: Westview Press
ProductGroup: Book
Binding: Paperback

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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience)

Spikes: Exploring the Neural Code (Computational Neuroscience)

Neural Networks for Pattern Recognition

Information Theory, Inference & Learning Algorithms

ASIN: 0201515601

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This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.

Customer Reviews:

Clear and logical exposition.......2007-08-18

It's not the latest book on this topic, so today, there are other texts that have more recent developments to be sure. I originally read this text about 15 years ago. But what I got from this book, that I didn't get from most, are important insights and clear understanding of the material that's covered. The authors have a deep understanding, and have teaching as their goal in writing. Most other texts in this area are lacking in one or both of those characteristics, and aren't worth the paper they are printed on.

Introduction to the Theory of Neural Computation.......2000-10-06

This book is written from a mathematical perspective. The book introduces the Hopfield Neural Network with history and applications. The authors solve the network problem and develop the Hebb Rule. Links are made to Ising Spin models and stochastic problems. I find this book to be one of the best written mathematical guides for Neural Networks.

A Broad Survey.......1997-11-08

This was a good survey, and well-grounded mathematically. It is kind of scattershot, and if you primarily want to do practical projects like predicting financial markets, a lot of the sections won't be relevant. But if you want a broad-based approach, emphasizing a variety of network designs fro different purposes, this book is very good.

Computation in Cells and Tissues: Perspectives and Tools of Thought (Natural Computing Series)

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Computation in Cells and Tissues: Perspectives and Tools of Thought (Natural Computing Series)

Manufacturer: Springer
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Binding: Hardcover

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ASIN: 3540003584

Book Description

This book deals with key issues in the emerging interdisciplinary area involving cellular systems, computational modelling, and biologically inspired computing. This highly multidisciplinary book offers a unique blend of topical contributions that are written by biologists, computer scientists and mathematicians with non-expert readers in mind. It reflects important trends and developments in this exciting field of science.

The volume can serve as a textbook and reference book for advanced students and computer scientists, biologists, and mathematicians.

Biology and Computation: A Physicist's Choice (Advanced Series in Neuroscience, Vol 3)

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Biology and Computation: A Physicist's Choice (Advanced Series in Neuroscience, Vol 3)
H. Gutfreund
Manufacturer: World Scientific Pub Co Inc
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Binding: Paperback

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This book provides a comprehensive review of the works in the rapidly evolving field of neural networks and brain studies. Its purpose is two-fold: to help physicists entering this field to get a broader view of the context of the domain, and to help scientists of other disciplines to reach a better understanding of the physicists' contributions within a context of perspectives they can relate to.
Included in the volume are 68 carefully selected, high quality reprints to provide the volume with both breadth and depth. It is organized into 5 sections and 22 chapters, both the sections and chapters being preceded by introductory comments by the editors.

Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience)

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Single Neuron Computational Modeling
How smart is a neuron?
brief & comprehensive
Excellent, a good place to start
it works

Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience)
Christof Koch
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Hardcover

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ASIN: 0195104919

Book Description

Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium- and potassium-currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

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Single Neuron Computational Modeling.......2004-06-11

This is the main book, the "Bible", on single neuron and ion channel computational modeling. Plenty of theory & rigor here! Professor Koch, with CalTech, models single ion channel function, dendrite, dendrite tree function, cable theory, stocastic theories, integrate-fire model, the Poisson model, and discusses how single neurons work together inside the brain. It is worth owning both as a reference book and to use in the laboratory. Dr. Koch has written many other books, but I think this stands out as his best. Methods in Neuronal Modeling 2nd edition is also very good. Koch's writings are complementary, but are not redundant. One can read this book without a problem if you know Calculus.

How smart is a neuron?.......2004-04-15

For young scientists who are interested in understanding the dynamics of the human brain this change in collective attitude is of profound significance, to which Koch's book provides an ideal introduction.Written in a precise yet easy style, the 21 chapters of Biophysics of Computation begin at the beginning, introducing the reader to elementary electrical properties of membrane patches, linear cable theory and the properties of passive dendritic trees. These introductory chapters are followed by two on the properties of synapses and the various ways that synapses can interact to perform logic on passive dendritic trees. Then the Hodgkin-Huxley formulation for impulse propagation on a single fibre is discussed in detail, and various simplifying models are presented. As a basis for the Hodgkin-Huxley description the present
understanding of ionic channels is reviewed, emphasizing the importance of calcium currents. Further chapters discuss linearization of the H-H equations for small amplitude behavior; present a careful examination of ionic diffusion processes; and describe electrochemical properties of dendritic spines, synaptic plasticity, simple neural models, stochastic neural models and the properties of bursting cells. Just about every facet of currently available neural knowledge is touched upon, with appropriate references to a carefully selected bibliography that will help the diligent novice delve deeply into whatever aspect of neural information processing he or she chooses.

All of the above comprises an extended introduction to Chapters 17 to 19, which: `synthesize the previously learned lessons into a complete account of the events occurring in realistic dendritic trees with all of their attendant nonlinearities'. `We will see', the author writes, `that dendrites can indeed be very powerful, nontraditional computational devices, implementing a number of continuous operations.' Thus Biophysics of Computation offers a definitive statement for the direction in which the neural research of the new century should go. Chapter 20, the penultimate, discusses several speculations for non-neural computation in the brain, ranging from molecular computing below the level of a single neuron to the effects of chemical diffusants (nitric oxide, calcium ions, carbon monoxide, etc.) on large numbers of neurons. Although this entire area has been neglected by most of the neuroscience community, Koch points out that there are no good reasons for doing so. As we enter the new century, neuroscientists should keep their minds open. Finally, in the summary of Chapter 21, seven problems for future research projects are listed, emphasizing that the investigation of information processing in single neurons is very much a work in progress. It is of interest to examine these `strategic questions' as they reveal the author's intuitions about possible directions of future developments. (Note that these are not direct quotes, as I have taken the liberty of summarizing Koch's questions.)

(1) How can the operation of multiplication be implemented at the level of a single neuron?
(2) What are the sources of noise in a neural system and how does this noise influence the logical operation of a single neuron?
(3) How is the style of neural computation influenced by metabolic considerations?
(4) What is the function of the apical dendrite, which is a typical cortical structure?
(5) How and where does learning actually take place in a neural system?
(6) What are the functions of the dendritic trees, the forms of which vary so widely from neuron to neuron?
(7) How can we construct neural models that are sufficiently realistic to capture the essential functions of real neurons yet simple enough to allow large-scale computations of brain dynamics?

As these questions indicate, Koch is not merely concerned with understanding
what unusual behaviours the neuron does or might exhibit. His broad aim is to comprehend the relation between this behavioural ability and the computational tasks that the neuron is called upon to perform. In his words:

``Thinking about brain style computation requires a certain frame of mind, related to but distinctly different from that of the biophysicist. For instance, how should we think of a chemical synapse? In terms of complicated pre- and post-synaptic elements? Ionic channels? Calcium binding proteins? Or as a non-reciprocal and stochastic switching device that transmits a binary signal rapidly between two neurons and remembers its history of usage? The answer is that we must be concerned with both aspects, with biophysics as well as computation.''

This excellent book is evidently a labour of love, stemming from the author's 1982 doctoral thesis on information processing in dendritic trees. As far as I can tell all relevant aspects of neural processing are considered, with what seem to me to be just the proper amounts of emphasis. The writing style is precise and rigorous without being stuffy, and the many references to a fifty-page bibliography will be of enormous value to young researchers starting out in this field.

In addition to its obvious value for those engaged in experimental, theoretical or numerical studies of neuronal behaviour Biophysics of Computation would also work well as the text for an introductory course in neural dynamics, perhaps as part of a neuroscience program.

Alwyn Scott
http://personal.riverusers.com/~rover/

brief & comprehensive.......2001-07-07

This book attempts to integrate bits from papers & other textbooks. Incorporated in the book are all but the most oft-discussed topics in neurophysics.

We don't know much about biological neurons. We don't really understand how they perform computation. Yet we have some models, approximations of the models, and theories of how the model neurons get organized to do computation. These are summarized in this book in a breif & comprehensive manner.

Some notes: 1) Portions of the book may be found in greater detail elsewhere. 2) The book is more about biophysics than compuation.

Excellent, a good place to start.......2000-04-30

This is a fine comprehensive book. However, it might be helpful to bear in mind, as you study it, that although it was published in late 1998, a few fundamental principles presented in the early chapters were originally developed a long time ago. In particular, the Hodgkin Huxley Katz picture of the neuron was developed in the heroic period of the 1950s after the introduction of the voltage clamp. It is a good model but it may not be the whole story, and could change in important ways as we learn more about the molecular structure of ion channels.

The possibility exists that the neuron is a multichannel device, a cable rather than a wire. The model is attractive because a multichannel nerve would enable us to think as fast as we do. Because nerve impulses are so very slow moving, each successive impulse might, (contrary to everything we thought we knew) be rich in information. A multichannel neuron has the power to convey, with each single all-or-nothing impulse, graded information. For example, to 20 discrete channels, one can assign 20 distinct tiers of meaning, and each channel can thus "mean" a level of intensity between 1 and 20. The phenomenon can easily escape detection because such a neuron appears, to conventional instruments, to convey only the classically blank, binary impulse that is so confidently presented to us on the first page of every neurobiology text, and in summary in this book as well.

To create a continuous longitudinal information channel running the full length of an axon membrane, one would simply link each ion portal to its next door neighbor. A conformation change in one portal induces a conformation change in the next in line. A domino effect more intuitively satisfying, perhaps, than the familiar waveguide or cable models of membrane depolarization reiterated here.

One can visualize many parallel tracks, a corduroy membrane. Possibly linear, possibly helical. Linked receptors are commonplace. The molecular structure of the potassium channel has been published recently, and so we are now finally working at the level where a multichannel membrane can be detected. It is a theoretical construct but if each single impulse carries information, then the computational burden on the nervous system is vastly reduced, and the physiological meaning of intensively studied structures like the synapse suddenly changes. The meaning of several of the models presented in this book also changes, often in quite intriguing ways.

it works.......2000-01-16

although a bit long, the author gets his point across in an if not literary, at least interesting style of writing. I especially found chapter 2 interesting because the introduction is easy to read and gives you just enough background for non-neurology dudes to get the hang of the rest of the book. the topic is one that has interested me for many years, and has gotten me seriously thinking about changing my college major. very interesting book. a must read.

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Computation and Neural Systems

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 079239349X

Book Description

Computational neuroscience is best defined by its focus on understanding the nervous systems as a computational device rather than by a particular experimental technique. Accordinlgy, while the majority of the papers in this book describe analysis and modeling efforts, other papers describe the results of new biological experiments explicitly placed in the context of computational issues. The distribution of subjects in Computation and Neural Systems reflects the current state of the field. In addition to the scientific results presented here, numerous papers also describe the ongoing technical developments that are critical for the continued growth of computational neuroscience. Computation and Neural Systems includes papers presented at the First Annual Computation and Neural Systems meeting held in San Francisco, CA, July 26--29, 1992.

Computation in Neurons and Neural Systems

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Computation in Neurons and Neural Systems

Manufacturer: Springer
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Binding: Hardcover

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ASIN: 0792394658

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Computation in Neurons and Neural Systems contains the collected papers of the 1993 Conference on Computation and Neural Systems which was held between July 31--August 7, in Washington, DC. These papers represent a cross-section of the state-of-the-art research work in the field of computational neuroscience, and includes coverage of analysis and modeling work as well as results of new biological experimentation.

Computational Neuroscience: Trends in Research 1995 (International Review of Neurobiology)

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Computational Neuroscience: Trends in Research 1995 (International Review of Neurobiology)
Calif.) Conference on Computation and Neural Systems (4th : 1995 Monterey
Manufacturer: Academic Pr
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Binding: Paperback

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California Institute of Technology, Pasadena. Peer-reviewed papers presented at the Fourth Annual Computation and Neural Systems meeting, held in Monterey, California, July 11-15, 1995. Research on the relationship between the structure of the nervous system and its function. 170 contributors, 85 U.S.

Computers and Computations in the Neurosciences (Methods in Neurosciences)

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Computers and Computations in the Neurosciences (Methods in Neurosciences)

Manufacturer: Academic Pr
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Binding: Hardcover

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Connectionist Models of Learning, Development and Evolution: Proceedings of the Sixth Neural Computation and Psychology Workshop, Liege, Belgium, 16-18 ... 2000 (Perspectives in Neural Computing)

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Connectionist Models of Learning, Development and Evolution: Proceedings of the Sixth Neural Computation and Psychology Workshop, Liege, Belgium, 16-18 ... 2000 (Perspectives in Neural Computing)

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ASIN: 1852333545

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Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena.With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections:The neural basis of cognitionDevelopment and category learningImplicit learningSocial cognition EvolutionSemanticsCovering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.

Foundations and Tools for Neural Modeling: International Work-Conference on Artificial and Natural Neural Networks, IWANN'99, Alicante, Spain, June 2-4, ... Volume I (Lecture Notes in Computer Science)

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Foundations and Tools for Neural Modeling: International Work-Conference on Artificial and Natural Neural Networks, IWANN'99, Alicante, Spain, June 2-4, ... Volume I (Lecture Notes in Computer Science)

Manufacturer: Springer
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ASIN: 3540660690

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This book constitutes, together with its compagnion LNCS 1607, the refereed proceedings of the International Work-Conference on Artificial and Natural Neural Networks, IWANN'99, held in Alicante, Spain in June 1999.
The 89 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to foundational issues of neural computation and tools for neural modeling. The papers are organized in parts on neural modeling: biophysical and structural models; plasticity phenomena: maturing, learning, and memory; and artificial intelligence and cognitive neuroscience.

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