Neurodynamics - Proceedings Of The 9th Summer Workshop PDF Download
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Author: Heinz-dietrich Doebner
Publisher: World Scientific
ISBN: 9814555541
Category :
Languages : en
Pages : 246
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Book Description
This volume presents applications of mathematical techniques for modelling and performance analysis of neural networks. The collection of articles is motivated by the observation that the theory of neural network dynamics, i.e. Neurodynamics, still has to be given a thorough mathematical foundation. Therefore, the volume comprises research work on different mathematical approaches to neural networks; analytical and numerical techniques of dynamical systems theory, geometrical techniques, and methods of statistical physics. Articles analyse dynamics of neural netwroks in general or concentrate on specific network models of biological or neurocomputing origin. A few of the articles serve as a good introduction to these subjects.
Author: Heinz-dietrich Doebner
Publisher: World Scientific
ISBN: 9814555541
Category :
Languages : en
Pages : 246
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Book Description
This volume presents applications of mathematical techniques for modelling and performance analysis of neural networks. The collection of articles is motivated by the observation that the theory of neural network dynamics, i.e. Neurodynamics, still has to be given a thorough mathematical foundation. Therefore, the volume comprises research work on different mathematical approaches to neural networks; analytical and numerical techniques of dynamical systems theory, geometrical techniques, and methods of statistical physics. Articles analyse dynamics of neural netwroks in general or concentrate on specific network models of biological or neurocomputing origin. A few of the articles serve as a good introduction to these subjects.
Author: Heinz Dietrich Doebner
Publisher:
ISBN: 9789814538244
Category : COMPUTERS
Languages : en
Pages : 233
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Book Description
Author: John Milton
Publisher: Springer Nature
ISBN: 3030695794
Category : Mathematics
Languages : en
Pages : 650
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Book Description
The second edition of Mathematics as a Laboratory Tool reflects the growing impact that computational science is having on the career choices made by undergraduate science and engineering students. The focus is on dynamics and the effects of time delays and stochastic perturbations (“noise”) on the regulation provided by feedback control systems. The concepts are illustrated with applications to gene regulatory networks, motor control, neuroscience and population biology. The presentation in the first edition has been extended to include discussions of neuronal excitability and bursting, multistability, microchaos, Bayesian inference, second-order delay differential equations, and the semi-discretization method for the numerical integration of delay differential equations. Every effort has been made to ensure that the material is accessible to those with a background in calculus. The text provides advanced mathematical concepts such as the Laplace and Fourier integral transforms in the form of Tools. Bayesian inference is introduced using a number of detective-type scenarios including the Monty Hall problem.
Author: Paul Smolensky
Publisher: Psychology Press
ISBN: 1134772947
Category : Psychology
Languages : en
Pages : 865
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Book Description
Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.
Author:
Publisher:
ISBN:
Category : Malaysian literature (English)
Languages : en
Pages : 644
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Book Description
Author:
Publisher:
ISBN:
Category : Conference proceedings
Languages : en
Pages : 856
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Author:
Publisher:
ISBN:
Category : Engineering
Languages : en
Pages : 836
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Book Description
Author: Martin T. Hagan
Publisher:
ISBN: 9789812403766
Category : Neural networks (Computer science)
Languages : en
Pages :
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Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Simon Haykin
Publisher:
ISBN: 9788178083001
Category : Neural networks (Computer science)
Languages : en
Pages : 842
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Book Description
