Self-Organization and Associative Memory


Book Description

Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of "neural computing", or "neural networks", as the previous neural models are nowadays called. The incentive, of course, has been to develop new com puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in formation processing principles of the same type as those encountered in the brain. All new ideas for the "neural computers" are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer tain lines of thought, disciplines, and criteria should be followed. It is the pur pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con tents is taken over from the first edition.




Self-Organization and Associative Memory


Book Description

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-Organizing Maps


Book Description

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.




Selected Writings on Self-organization, Philosophy, Bioethics, and Judaism


Book Description

During the last thirty years, biophysicist and philosopher Henri Atlan has been a major voice in contemporary European philosophical and bio-ethical debates. In a massive oeuvre that ranges from biology and neural network theory to Spinoza's thought and the history of philosophy, and from artificial intelligence and information theory to Jewish mysticism and to contemporary medical ethics, Atlan has come to offer an exceptionally powerful philosophical argumentation that is as hostile to scientism as it is attentive to biology's conceptual and experimental rigor, as careful with concepts of rationality as it is committed to rethinking the human place in a radically determined yet forever changing world. --Book Jacket.




Self-Organizing Maps


Book Description

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.




Competition and Cooperation in Neural Nets


Book Description

The human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a "competition and cooperation" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers presented at a U. S. -Japan Joint Seminar on "Competition and Cooperation in Neural Nets" which was designed to catalyze better integration of theory and experiment in these areas. It was held in Kyoto, Japan, February 15-19, 1982, under the joint sponsorship of the U. S. National Science Foundation and the Japan Society for the Promotion of Science. Participants included brain theorists, neurophysiologists, mathematicians, computer scientists, and physicists. There are seven papers from the U. S.




Parallel Models of Associative Memory


Book Description

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, Emerging Properties, and Learning


Book Description

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.




Neurocomputing


Book Description

In bringing together seminal articles on the foundations of research, the first volume of Neurocomputing has become an established guide to the background of concepts employed in this burgeoning field. Neurocomputing 2 collects forty-one articles covering network architecture, neurobiological computation, statistics and pattern classification, and problems and applications that suggest important directions for the evolution of neurocomputing.James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University. Andras Pellionisz is a Research Associate Professor in the Department of Physiology and Biophysics at New York Medical Center and a Senior National Research Council Associate to NASA. Edward Rosenfeld is editor and publisher of the newsletters Intelligence and Medical Intelligence.