Self-Organization, Computational Maps, and Motor Control

Self-Organization, Computational Maps, and Motor Control PDF

Author: P.G. Morasso

Publisher: Elsevier

Published: 1997-03-19

Total Pages: 655

ISBN-13: 0080540910

DOWNLOAD EBOOK →

In the study of the computational structure of biological/robotic sensorimotor systems, distributed models have gained center stage in recent years, with a range of issues including self-organization, non-linear dynamics, field computing etc. This multidisciplinary research area is addressed here by a multidisciplinary team of contributors, who provide a balanced set of articulated presentations which include reviews, computational models, simulation studies, psychophysical, and neurophysiological experiments. The book is divided into three parts, each characterized by a slightly different focus: in part I, the major theme concerns computational maps which typically model cortical areas, according to a view of the sensorimotor cortex as "geometric engine" and the site of "internal models" of external spaces. Part II also addresses problems of self-organization and field computing, but in a simpler computational architecture which, although lacking a specialized cortical machinery, can still behave in a very adaptive and surprising way by exploiting the interaction with the real world. Finally part III is focused on the motor control issues related to the physical properties of muscular actuators and the dynamic interactions with the world. The reader will find different approaches on controversial issues, such as the role and nature of force fields, the need for internal representations, the nature of invariant commands, the vexing question about coordinate transformations, the distinction between hierachiacal and bi-directional modelling, and the influence of muscle stiffness.

Self-organizing Map Formation

Self-organizing Map Formation PDF

Author: Klaus Obermayer

Publisher: MIT Press

Published: 2001

Total Pages: 472

ISBN-13: 9780262650601

DOWNLOAD EBOOK →

This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang

Self-Organization in the Evolution of Speech

Self-Organization in the Evolution of Speech PDF

Author: Pierre-Yves Oudeyer

Publisher: OUP Oxford

Published: 2006-04-06

Total Pages: 192

ISBN-13: 0191516104

DOWNLOAD EBOOK →

Speech is the principal supporting medium of language. In this book Pierre-Yves Oudeyer considers how spoken language first emerged. He presents an original and integrated view of the interactions between self-organization and natural selection, reformulates questions about the origins of speech, and puts forward what at first sight appears to be a startling proposal - that speech can be spontaneously generated by the coupling of evolutionarily simple neural structures connecting perception and production. He explores this hypothesis by constructing a computational system to model the effects of linking auditory and vocal motor neural nets. He shows that a population of agents which used holistic and unarticulated vocalizations at the outset are inexorably led to a state in which their vocalizations have become discrete, combinatorial, and categorized in the same way by all group members. Furthermore, the simple syntactic rules that have emerged to regulate the combinations of sounds exhibit the fundamental properties of modern human speech systems. This original and fascinating account will interest all those interested in the evolution of speech.

Neural Nets WIRN VIETRI-98

Neural Nets WIRN VIETRI-98 PDF

Author: Maria Marinaro

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 389

ISBN-13: 1447108116

DOWNLOAD EBOOK →

From its early beginnings in the fifties and sixties, the field of neural networks has been steadily developing to become one of the most interdisciplinary areas of research within computer science. This volume contains selected papers from WIRN Vietri-98, the 10th Italian Workshop on Neural Nets, 21-23 May 1998, Vietri sul Mare, Salerno, Italy. This annual event, sponsored amongst others by the IEEE Neural Network Council and the INNS/SIG Italy, brings together the best of research from all over the world. The papers cover a range of key topics within neural networks, including pattern recognition, signal processing, hybrid systems, mathematical models, hardware and software design, and fuzzy techniques. It also includes two review talks on a Morpho-Functional Model to Describe Variability Found at Hippocampal Synapses and Neural Networks and Speech Processing. By providing the reader with a comprehensive overview of recent research in this area, the volume makes a valuable contribution to the Perspectives in Neural Computing Series.

Self-Organizing Maps

Self-Organizing Maps PDF

Author: Teuvo Kohonen

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 372

ISBN-13: 3642976107

DOWNLOAD EBOOK →

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.

Kohonen Maps

Kohonen Maps PDF

Author: E. Oja

Publisher: Elsevier

Published: 1999-07-02

Total Pages: 400

ISBN-13: 9780080535296

DOWNLOAD EBOOK →

The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm. The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.

Human and Machine Perception

Human and Machine Perception PDF

Author: Virginio Cantoni

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 329

ISBN-13: 1461559650

DOWNLOAD EBOOK →

The following are th€:" proceedings of the Second International Workshop on Human and Machine Perception held in Trabia, Italy, on July 21~25, 1996, under the auspices of two Institutions: the Cybernetic and Biophysics Group (GNCB) of the Italian National Research Council (CNR) and the 'Centro Interdipartimentale di Tecnologie della Conoscenza' ofPalenno University. A broad spectrum of topics are covered in this series, ranging from computer perception to psychology and physiology of perception (visual, auditory, tactile, etc.). The theme of this workshop was: "Human and Machine Perception: Information Fusion". The goal of information and sensory data fusion is to integrate internal knowledge with complementary and/or redundant information from many sensors to achieve (and maintain) a better knowledge of the environment. The mechanism behind the integration of information is one of the most difficult challenges in understanding human and robot perception. The workshop consisted of a pilot phase of eight leCtures introducing perception sensorialities in nature and artificial systems, and of five subsequent modules each consisting of two lectures (dealing with solutions in nature and machines respectively) and a panel discussion.

Quantum Machine Learning

Quantum Machine Learning PDF

Author: Siddhartha Bhattacharyya

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2020-06-08

Total Pages: 131

ISBN-13: 3110670704

DOWNLOAD EBOOK →

Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.

Adaptive Motion of Animals and Machines

Adaptive Motion of Animals and Machines PDF

Author: Hiroshi Kimura

Publisher: Springer Science & Business Media

Published: 2006-07-28

Total Pages: 298

ISBN-13: 4431313818

DOWNLOAD EBOOK →

• Motivation It is our dream to understand the principles of animals’ remarkable ability for adaptive motion and to transfer such abilities to a robot. Up to now, mechanisms for generation and control of stereotyped motions and adaptive motions in well-known simple environments have been formulated to some extentandsuccessfullyappliedtorobots.However,principlesofadaptationto variousenvironmentshavenotyetbeenclari?ed,andautonomousadaptation remains unsolved as a seriously di?cult problem in robotics. Apparently, the ability of animals and robots to adapt in a real world cannot be explained or realized by one single function in a control system and mechanism. That is, adaptation in motion is induced at every level from thecentralnervoussystemtothemusculoskeletalsystem.Thus,weorganized the International Symposium on Adaptive Motion in Animals and Machines(AMAM)forscientistsandengineersconcernedwithadaptation onvariouslevelstobebroughttogethertodiscussprinciplesateachleveland to investigate principles governing total systems. • History AMAM started in Montreal (Canada) in August 2000. It was organized by H. Kimura (Japan), H. Witte (Germany), G. Taga (Japan), and K. Osuka (Japan), who had agreed that having a small symposium on motion control, with people from several ?elds coming together to discuss speci?c issues, was worthwhile. Those four organizing committee members determined the scope of AMAM as follows.