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Self-Organization and Associative Memory

Self-Organization and Associative Memory
Author: Teuvo Kohonen
Publisher: Springer Science & Business Media
Total Pages: 325
Release: 2012-12-06
Genre: Medical
ISBN: 3642881637

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While the present edition is bibliographically the third one of Vol. 8 of the Springer Series in Information Sciences (IS 8), the book actually stems from Vol. 17 of the series Communication and Cybernetics (CC 17), entitled Associative Memory - A System-Theoretical Approach, which appeared in 1977. That book was the first monograph on distributed associative memories, or "content-addressable memories" as they are frequently called, especially in neural-networks research. This author, however, would like to reserve the term "content-addressable memory" for certain more traditional constructs, the memory locations of which are selected by parallel search. Such devices are discussed in Vol. 1 of the Springer Series in Information Sciences, Content-Addressable Memories. This third edition of IS 8 is rather similar to the second one. Two new discussions have been added: one to the end of Chap. 5, and the other (the L VQ 2 algorithm) to the end of Chap. 7. Moreover, the convergence proof in Sect. 5.7.2 has been revised.


Self-organization and Associative Memory

Self-organization and Associative Memory
Author: Teuvo Kohonen
Publisher: Springer Science & Business Media
Total Pages: 255
Release: 1984
Genre: Associative storage
ISBN: 9783540121657

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Self-Organizing Maps

Self-Organizing Maps
Author: Teuvo Kohonen
Publisher: Springer Science & Business Media
Total Pages: 514
Release: 2012-12-06
Genre: Science
ISBN: 3642569277

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The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About 4000 research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real world problems. Many fields of science have adopted the SOM as a standard analytical tool: statistics, signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. This new edition includes a survey of over 2000 contemporary studies to cover the newest results. Case examples are provided with detailed formulae, illustrations, and tables. Further, a new chapter on software tools for SOM has been included whilst other chapters have been extended and reorganised.


Parallel Models of Associative Memory

Parallel Models of Associative Memory
Author: Geoffrey E. Hinton
Publisher: Psychology Press
Total Pages: 350
Release: 2014-02-25
Genre: Psychology
ISBN: 1317785215

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This update of the 1981 classic on neural networks includes new commentaries by the authors that show how the original ideas are related to subsequent developments. As researchers continue to uncover ways of applying the complex information processing abilities of neural networks, they give these models an exciting future which may well involve revolutionary developments in understanding the brain and the mind -- developments that may allow researchers to build adaptive intelligent machines. The original chapters show where the ideas came from and the new commentaries show where they are going.


Self-Organization in Optical Systems and Applications in Information Technology

Self-Organization in Optical Systems and Applications in Information Technology
Author: Mikhail A. Vorontsov
Publisher: Springer Science & Business Media
Total Pages: 258
Release: 2012-12-06
Genre: Science
ISBN: 3642975259

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Contrary to monographs on non-linear optics this book concentrates on problems of self-organization in various important contexts. The reader learns how patterns in non-linear optical systems are created and what theoretical methods can be applied to describe them. Next, various aspects of pattern formation such as associative memory, information processing, spatio-temporal instability, photo refraction, and so on are treated. The book addresses graduate students and researchers in physics and optical engineering.


Self-Organization, Emerging Properties, and Learning

Self-Organization, Emerging Properties, and Learning
Author: Agnessa Babloyantz
Publisher: Springer Science & Business Media
Total Pages: 317
Release: 2012-12-06
Genre: Science
ISBN: 1461537789

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This volume contains the proceedings of the workshop held in March 1990 at Austin, Texas on Self-Organization, Emerging Properties and Learning. The workshop was co-sponsored by NATO Scientific Affairs Division, Solvay Institutes of Physics and Chemistry, the University of Texas at Austin and IC2 Institute at Austin. It gathered representatives from a large spectrum of scientific endeavour. The subject matter of self-organization extends over several fields such as hydrodynamics, chemistry, biology, neural networks and social sciences. Several key concepts are common to all these different disciplines. In general the self-organization processes in these fields are described in the framework of the nonlinear dynamics, which also governs the mechanisms underlying the learning processes. Because of this common language, it is expected that any progress in one area could benefit other fields, thus a beneficial cross fertilization may result. In last two decades many workshops and conferences had been organized in various specific fields dealing with self-organization and emerging properties of systems. The aim of the workshop in Austin was to bring together researchers from seemingly unrelated areas and interested in self-organization, emerg{ng properties and learning capabilities of interconnected multi-unit systems. The hope was to initiate interesting exchange and lively discussions. The expectations of the organiziers are materialized in this unusual collection of papers, which brings together in a single volume representative research from many related fields. Thus this volume gives to the reader a wider perspective over the generality and ramifications of the key concepts of self organization.


Self-Organizing Maps

Self-Organizing Maps
Author: Teuvo Kohonen
Publisher: Springer Science & Business Media
Total Pages: 372
Release: 2012-12-06
Genre: Science
ISBN: 3642976107

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The book we have at hand is the fourth monograph I wrote for Springer Verlag. The previous one named "Self-Organization and Associative Mem ory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works re viewed in Chap. 9. The new results obtained in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book. I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step. I became interested in neural networks around 1960, but could not in terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano.


Pattern Recognition by Self-organizing Neural Networks

Pattern Recognition by Self-organizing Neural Networks
Author: Gail A. Carpenter
Publisher: MIT Press
Total Pages: 724
Release: 1991
Genre: Computers
ISBN: 9780262031769

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Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms exist in the brain. Contributors: J.-P. Banquet, G.A. Carpenter, S.Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C.von der Malsburg, C.L. Winter.


Learning As Self-organization

Learning As Self-organization
Author: Karl H. Pribram
Publisher: Psychology Press
Total Pages: 617
Release: 2013-06-17
Genre: Psychology
ISBN: 1134997019

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A year before his death, B.F. Skinner wrote that "There are two unavoidable gaps in any behavioral account: one between the stimulating action of the environment and the response of the organism and one between consequences and the resulting change in behavior. Only brain science can fill those gaps. In doing so, it completes the account; it does not give a different account of the same thing." This declaration ended the epoch of radical behaviorism to the extent that it was based on the doctrine of the "empty organism," the doctrine that a behavioral science must be constructed purely on its own level of investigation. However, Skinner was not completely correct in his assessment. Brain science on its own can no more fill the gaps than can single level behavioral science. It is the relation between data and formulations developed in the brain and the behavioral sciences that is needed. This volume is the result of The Fourth Appalachian Conference on Behavioral Neurodynamics, the first three of which were aimed at filling Skinner's first gap. Taking the series in a new direction, the aim of the fourth and subsequent conferences is to explore the second of the gaps in the behavioral account noted by Skinner. The aim of this conference was to explore the aphorism: The motivation for learning is self organization. In keeping with this aim and in the spirit of previous events, this conference's mission was to acquaint scientists working in one discipline with the work going on in other disciplines that is relevant to both. As a result, it brought together those who are making advances on the behavioral level -- mainly working in the tradition of operant conditioning -- and those working with brains -- mainly amygdala, hippocampus, and far frontal cortex.


Self-Adaptive Systems for Machine Intelligence

Self-Adaptive Systems for Machine Intelligence
Author: Haibo He
Publisher: John Wiley & Sons
Total Pages: 189
Release: 2011-09-15
Genre: Computers
ISBN: 1118025598

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This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.