An Information-Theoretic Approach to Neural Computing

Download An Information-Theoretic Approach to Neural Computing PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 1461240166
Total Pages : 265 pages
Book Rating : 4.4/5 (612 download)

DOWNLOAD NOW!


Book Synopsis An Information-Theoretic Approach to Neural Computing by : Gustavo Deco

Download or read book An Information-Theoretic Approach to Neural Computing written by Gustavo Deco and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Information Theoretic Neural Computation

Download Information Theoretic Neural Computation PDF Online Free

Author :
Publisher : World Scientific
ISBN 13 : 9814494275
Total Pages : 219 pages
Book Rating : 4.8/5 (144 download)

DOWNLOAD NOW!


Book Synopsis Information Theoretic Neural Computation by : Ryotaro Kamimura

Download or read book Information Theoretic Neural Computation written by Ryotaro Kamimura and published by World Scientific. This book was released on 2002-12-19 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to develop new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. α-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind.

Information Theoretic Neural Computation

Download Information Theoretic Neural Computation PDF Online Free

Author :
Publisher : World Scientific
ISBN 13 : 9810240759
Total Pages : 219 pages
Book Rating : 4.8/5 (12 download)

DOWNLOAD NOW!


Book Synopsis Information Theoretic Neural Computation by : Ryotaro Kamimura

Download or read book Information Theoretic Neural Computation written by Ryotaro Kamimura and published by World Scientific. This book was released on 2002 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to develope new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. a-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind.

Introduction To The Theory Of Neural Computation

Download Introduction To The Theory Of Neural Computation PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 0429968213
Total Pages : 352 pages
Book Rating : 4.4/5 (299 download)

DOWNLOAD NOW!


Book Synopsis Introduction To The Theory Of Neural Computation by : John A. Hertz

Download or read book Introduction To The Theory Of Neural Computation written by John A. Hertz and published by CRC Press. This book was released on 2018-03-08 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.

Discrete Neural Computation

Download Discrete Neural Computation PDF Online Free

Author :
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 444 pages
Book Rating : 4.3/5 (91 download)

DOWNLOAD NOW!


Book Synopsis Discrete Neural Computation by : Kai-Yeung Siu

Download or read book Discrete Neural Computation written by Kai-Yeung Siu and published by Prentice Hall. This book was released on 1995 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by the three leading authorities in the field, this book brings together -- in one volume -- the recent developments in discrete neural computation, with a focus on neural networks with discrete inputs and outputs. It integrates a variety of important ideas and analytical techniques, and establishes a theoretical foundation for discrete neural computation. Discusses the basic models for discrete neural computation and the fundamental concepts in computational complexity; establishes efficient designs of threshold circuits for computing various functions; develops techniques for analyzing the computational power of neural models. A reference/text for computer scientists and researchers involved with neural computation and related disciplines.

Information Theoretic Learning

Download Information Theoretic Learning PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 1441915702
Total Pages : 538 pages
Book Rating : 4.4/5 (419 download)

DOWNLOAD NOW!


Book Synopsis Information Theoretic Learning by : Jose C. Principe

Download or read book Information Theoretic Learning written by Jose C. Principe and published by Springer Science & Business Media. This book was released on 2010-04-06 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Principles of Neural Information Theory

Download Principles of Neural Information Theory PDF Online Free

Author :
Publisher :
ISBN 13 : 9780993367922
Total Pages : 214 pages
Book Rating : 4.3/5 (679 download)

DOWNLOAD NOW!


Book Synopsis Principles of Neural Information Theory by : James V Stone

Download or read book Principles of Neural Information Theory written by James V Stone and published by . This book was released on 2018-05-15 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this richly illustrated book, it is shown how Shannon's mathematical theory of information defines absolute limits on neural efficiency; limits which ultimately determine the neuroanatomical microstructure of the eye and brain. Written in an informal style this is an ideal introduction to cutting-edge research in neural information theory.

Information Theory, Inference and Learning Algorithms

Download Information Theory, Inference and Learning Algorithms PDF Online Free

Author :
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)

DOWNLOAD NOW!


Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book 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. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art 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, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

An Introduction to Computational Learning Theory

Download An Introduction to Computational Learning Theory PDF Online Free

Author :
Publisher : MIT Press
ISBN 13 : 9780262111935
Total Pages : 230 pages
Book Rating : 4.1/5 (119 download)

DOWNLOAD NOW!


Book Synopsis An Introduction to Computational Learning Theory by : Michael J. Kearns

Download or read book An Introduction to Computational Learning Theory written by Michael J. Kearns and published by MIT Press. This book was released on 1994-08-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

Neural Networks and Analog Computation

Download Neural Networks and Analog Computation PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 146120707X
Total Pages : 193 pages
Book Rating : 4.4/5 (612 download)

DOWNLOAD NOW!


Book Synopsis Neural Networks and Analog Computation by : Hava T. Siegelmann

Download or read book Neural Networks and Analog Computation written by Hava T. Siegelmann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.

Biophysics of Computation

Download Biophysics of Computation PDF Online Free

Author :
Publisher : Oxford University Press
ISBN 13 : 0195181999
Total Pages : 587 pages
Book Rating : 4.1/5 (951 download)

DOWNLOAD NOW!


Book Synopsis Biophysics of Computation by : Christof Koch

Download or read book Biophysics of Computation written by Christof Koch and published by Oxford University Press. This book was released on 2004-10-28 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Information-Theoretic Aspects of Neural Networks

Download Information-Theoretic Aspects of Neural Networks PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 100014125X
Total Pages : 233 pages
Book Rating : 4.0/5 (1 download)

DOWNLOAD NOW!


Book Synopsis Information-Theoretic Aspects of Neural Networks by : P. S. Neelakanta

Download or read book Information-Theoretic Aspects of Neural Networks written by P. S. Neelakanta and published by CRC Press. This book was released on 2020-09-23 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation

Download Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3030304876
Total Pages : 839 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!


Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation by : Igor V. Tetko

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation written by Igor V. Tetko and published by Springer Nature. This book was released on 2019-09-09 with total page 839 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Novelty, Information and Surprise

Download Novelty, Information and Surprise PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 3642290752
Total Pages : 260 pages
Book Rating : 4.6/5 (422 download)

DOWNLOAD NOW!


Book Synopsis Novelty, Information and Surprise by : Günther Palm

Download or read book Novelty, Information and Surprise written by Günther Palm and published by Springer Science & Business Media. This book was released on 2012-08-30 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book offers a new approach to information theory that is more general then the classical approach by Shannon. The classical definition of information is given for an alphabet of symbols or for a set of mutually exclusive propositions (a partition of the probability space Ω) with corresponding probabilities adding up to 1. The new definition is given for an arbitrary cover of Ω, i.e. for a set of possibly overlapping propositions. The generalized information concept is called novelty and it is accompanied by two new concepts derived from it, designated as information and surprise, which describe "opposite" versions of novelty, information being related more to classical information theory and surprise being related more to the classical concept of statistical significance. In the discussion of these three concepts and their interrelations several properties or classes of covers are defined, which turn out to be lattices. The book also presents applications of these new concepts, mostly in statistics and in neuroscience.

Neuronal Dynamics

Download Neuronal Dynamics PDF Online Free

Author :
Publisher : Cambridge University Press
ISBN 13 : 1107060834
Total Pages : 591 pages
Book Rating : 4.1/5 (7 download)

DOWNLOAD NOW!


Book Synopsis Neuronal Dynamics by : Wulfram Gerstner

Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

Theoretical Advances in Neural Computation and Learning

Download Theoretical Advances in Neural Computation and Learning PDF Online Free

Author :
Publisher : Springer Science & Business Media
ISBN 13 : 1461526965
Total Pages : 482 pages
Book Rating : 4.4/5 (615 download)

DOWNLOAD NOW!


Book Synopsis Theoretical Advances in Neural Computation and Learning by : Vwani Roychowdhury

Download or read book Theoretical Advances in Neural Computation and Learning written by Vwani Roychowdhury and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.

Introduction To The Theory Of Neural Computation

Download Introduction To The Theory Of Neural Computation PDF Online Free

Author :
Publisher : Westview Press
ISBN 13 :
Total Pages : 354 pages
Book Rating : 4.:/5 (318 download)

DOWNLOAD NOW!


Book Synopsis Introduction To The Theory Of Neural Computation by : John A. Hertz

Download or read book Introduction To The Theory Of Neural Computation written by John A. Hertz and published by Westview Press. This book was released on 1991-06-24 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lecture notes volume I.