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Monday, July 27, 2020 | History

5 edition of information-theoretic approach to neural computing found in the catalog.

information-theoretic approach to neural computing

by Gustavo Deco

  • 115 Want to read
  • 10 Currently reading

Published by Springer in New York .
Written in English

    Subjects:
  • Neural networks (Computer science)

  • Edition Notes

    Includes bibliographical references (p. [243]-257) and index.

    StatementGustavo Deco, Dragan Obradovic.
    SeriesPerspectives in neural computing
    ContributionsObradovic, Dragan.
    Classifications
    LC ClassificationsQA76.87 .D47 1996
    The Physical Object
    Paginationxiii, 261 p. :
    Number of Pages261
    ID Numbers
    Open LibraryOL811391M
    ISBN 100387946667
    LC Control Number95048306

    Information Theory for Analysis of Neural Data. Information theory is a “mathematical theory of communication” developed in the 's by Claude Shannon at Bell Labs (Cover and Thomas, ; Shannon, ).It formalises, in a mathematically rigorous way, a measure of “information” in a system with applications to coding and transmission of that information. Chapters in Books: 35 Books: 1) "An Information-Theoretic Approach to Neural Computing" G. Deco and D. Obradovic Springer Verlag,, New York 2) "Information Dynamics: Foundations and Applications" G. Deco and B. Schürmann Springer Verlag, New York, 3) "Computational Neuroscience of Vision" E. Rolls and G. Deco.

    In particular, we show that based on our approach, we can give an analytical expression to the weights computed in deep neural networks. This gives us the option of either to compute these weights with a separate routine different from the standard training procedure of neural networks or to use the computation results of a neural network for. Information theory, the mathematical theory of communication in the presence of noise, is playing an increasingly important role in modern quantitative neuroscience. It makes it possible to treat neural systems as stochastic communication channels and gain valuable, quantitative insights into their sensory coding function. These techniques provide results on how neurons encode stimuli in a way.

    Becker, S. & Hinton, G. (). A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, , Google Scholar Cross Ref; Chaitin, G. J. (). Information-theoretic limitations of formal aystems. Journal of the Association for Computing Machinery, 21, Google Scholar Digital Library. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. An Information Theoretic Approach To Neural Computing. Author: Gustavo Deco ISBN: Genre.


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Information-theoretic approach to neural computing by Gustavo Deco Download PDF EPUB FB2

Introduction Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint.

They show how this perspective provides new insights into the design theory of neural networks. An Information-Theoretic Approach to Neural Computing (Perspectives in Neural Computing) [Gustavo Deco, Dragan Obradovic] on *FREE* shipping on qualifying offers.

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.

Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the.

From the Publisher: Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. Summary: Neural networks provide a powerful new technology to model and control nonlinear and complex systems.

In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. A detailed formulation of neural networks from the information-theoretic viewpoint.

Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. - Neural Network Algorithms and PCA 51 Information Theoretic Approach: Infomax 57 Minimization of Information Loss Principle and Infomax Principle 58 Upper Bound of Information Loss 59 Information Capacity as a Lyapunov Function of the General Stochastic Approximation 61 Independent Component Analysis.

Several information-theoretic principles, such as information maximization and minimization, have been proposed to describe neural information processing. Linsker sated in his infomax principle, that connection weights were modified so as to maximize mutual information between input and output layers.

In this study, a realistic ultrasound RF simulator, described in Sectionwas used to generate echo envelopes with different scattering density, spacing and envelope histograms resemble specific probability density functions.

Figs. 1a and b show the histograms for very high and very low density, spacing and former appears to have a Rayleigh form, while. Theoretical or experimental analyses, using information–theoretic quantities, are welcome. - Dynamic behavior of CI methods (e.g., information propagation in evolutionary computing, information–theoretic aspects of neural network training).

Theoretical or experimental analyses, using information–theoretic quantities, are welcome. Neural Computing Surveys. Books Evolutionary Learning Algorithms for Neural Adaptive Control Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering An Information-Theoretic Approach to Neural ComputingG.

Deco and D. Obradovic Neuronal Adaptation Theory. Quick Search in Books. Enter words / phrases / DOI / ISBN / keywords / authors / etc. Search Search. Quick Search anywhere. Enter words / phrases / DOI / ISBN / keywords / authors / etc.

Search Search. Advanced Search. 0 My Cart. Sign in. Skip main navigation. Close Drawer Menu Open Drawer Menu Home. Subject. All Subjects. Abstract. In this article we extend the (recently published) unsupervised information theoretic vector quantization approach based on the Cauchy–Schwarz-divergence for matching data and prototype densities to supervised learning and classification.

Besides information theoretic toolboxes designed primarily for neuroscientific data, there are also other open-source information theoretic packages not designed specifically for neural data.

One prominent example is the R package “entropy” 6, which implements plug-in estimates of the entropy and mutual information, as well as a number of. This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms.

ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances. 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. A three step approach is developed using indirect methods of calculating trans- information. Its estimation is affected by three separate factors: the model performance, the coordinate system, and.

- Buy Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics) book online at best prices in India on Read Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives (Information Science and Statistics) book reviews & author details and more at Free delivery on qualified s: 1.

Hulle of fers an information theoretic approach for learning. He recently wrote an interactiv e electronic book entitled Neural and Adaptive Systems: neural computing. This book has been cited by the following publications.

An Information-Theoretic Approach to Neural Computing. CrossRef; Google Scholar; Maass, Wolfgang and Natschläger, Thomas 3 - The dynamics of neural networks: a stochastic approach pp Get access. An Information-Theoretic Approach to Explainable Machine Learning.

03/01/ ∙ by Alexander Jung, et al. ∙ 0 ∙ share. A key obstacle to the successful deployment of machine learning (ML) methods to important application domains is the (lack of) explainability of predictions.

Explainable ML is challenging since explanations must be tailored (personalized) to individual users with.Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint.

They show how this perspective provides new insights into the design theory of neural networks.In the literature on information-theoretic bounded rationality (Ortega & Braun,), the objective in equation is known as the free energy F of the corresponding decision-making process.

In this form, the optimal posterior can be explicitly derived by determining the zeros of the functional derivative of F with respect to P.