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Author: P. S. Neelakanta Publisher: CRC Press ISBN: 100014125X Category : Technology & Engineering Languages : en Pages : 233
Book Description
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.
Author: P. S. Neelakanta Publisher: CRC Press ISBN: 100014125X Category : Technology & Engineering Languages : en Pages : 233
Book Description
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.
Author: P. S. Neelakanta Publisher: CRC Press ISBN: 1000102750 Category : History Languages : en Pages : 417
Book Description
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.
Author: P. S. Neelakanta Publisher: CRC Press ISBN: 9780849331985 Category : Computers Languages : en Pages : 416
Book Description
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.
Author: Gustavo Deco Publisher: Springer Science & Business Media ISBN: 1461240166 Category : Computers Languages : en Pages : 265
Book Description
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.
Author: Perambur S Neelakanta Publisher: World Scientific ISBN: 9811212902 Category : Science Languages : en Pages : 684
Book Description
This book on bioinformatics is designed as an introduction to the conventional details of genomics and proteomics as well as a practical comprehension text with an extended scope on the state-of-the-art bioinformatic details pertinent to next-generation sequencing, translational/clinical bioinformatics and vaccine-design related viral informatics.It includes four major sections: (i) An introduction to bioinformatics with a focus on the fundamentals of information-theory applied to biology/microbiology, with notes on bioinformatic resources, data bases, information networking and tools; (ii) a collection of annotations on the analytics of biomolecular sequences, with pertinent details presented on biomolecular informatics, pairwise and multiple sequences, viral sequence informatics, next-generation sequencing and translational/clinical bioinformatics; (iii) a novel section on cytogenetic and organelle bioinformatics explaining the entropy-theoretics of cellular structures and the underlying informatics of synteny correlations; and (iv) a comprehensive presentation on phylogeny and species informatics.The book is aimed at students, faculty and researchers in biology, health/medical sciences, veterinary/agricultural sciences, bioengineering, biotechnology and genetic engineering. It will be a useful companion for managerial personnel in the biotechnology and bioengineering industries as well as in health/medical science.
Author: Ryotaro Kamimura Publisher: World Scientific ISBN: 9810240759 Category : Computers Languages : en Pages : 219
Book Description
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.
Author: David J. C. MacKay Publisher: Cambridge University Press ISBN: 9780521642989 Category : Computers Languages : en Pages : 694
Book Description
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.
Author: Jose C. Principe Publisher: Springer Science & Business Media ISBN: 1441915702 Category : Computers Languages : en Pages : 538
Book Description
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.
Author: Adrian Tovar Lopez Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Neural networks as efficient as they are in practice, remain in several aspects still a mystery. Some of the most studied questions are: where does their generalization capabilities come from? What are the reason behind the existence of adversarial examples? In this thesis I use a formal mathematical representation of neural networks to investigate this questions. I also develop new algorithms based on the theory developed. The first par of the thesis is concerned with the generalization error which characterizes the gap between an algorithm's performance on test data versus performance on training data. I derive upper bounds on the generalization error in terms of a certain Wasserstein distance involving the distributions of input and the output under the assumption of a Lipschitz continuous loss function. Unlike mutual information-based bounds, these new bounds are useful for algorithms such as stochastic gradient descent. Moreover, I show that in some natural cases these bounds are tighter than mutual information-based bounds. In the second part of the thesis I study manifold learning. The goal is to learn a manifold that captures the inherent low-dimensionality of high-dimensional data. I present a novel training procedure to learn manifolds using neural networks. Parametrizing the manifold via a neural network with a low-dimensional input and a high-dimensional output. During training, I calculate the distance between the training data points and the manifold via a geometric projection and update the network weights so that this distance diminishes. The learned manifold is seen to interpolate the training data, analogous to autoencoders. Experiments show that the procedure leads to lower reconstruction errors for noisy inputs, and higher adversarial accuracy when used in manifold defense methods than those of autoencoders. In the final part of the thesis I propose an information bottleneck principle for causal time-series prediction. I develop variational bounds on the information bottleneck objective function that can be efficiently optimized using recurrent neural networks. Then implement an algorithm on simulated data as well as real-world weather-prediction and stock market-prediction datasets and show that these problems can be successfully solved using the new information bottleneck principle.
Author: P. S. Neelakanta
Author: P. S. Neelakanta
Author: P. S. Neelakanta
Author: P. S. Neelakanta
Author: Gustavo Deco
Author: Perambur S Neelakanta
Author: Daniel A. Roberts
Author: Ryotaro Kamimura
Author: David J. C. MacKay
Author: Jose C. Principe
Author: Adrian Tovar Lopez