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.

Self-awareness, self-belief, self-esteem, self-acceptance, self-love, self-renewal: how would you like to transform your "self"? Self-help guru Brian Mayne guides readers on an inward journey of personal discovery that will lead them to greater fulfillment and confidence. The results are amazing, yet his method is beautifully simple: Mayne explains how to create a "Self Map" that activates the whole brain and serves as a reminder of the person you’d like to become. This map literally acts like a subconscious command, and stimulates you to act upon your desires; by regularly meditating on it you can integrate your "low-self ego" with your "High-Self spirit" to become your authentic, natural True Self.

Self-awareness, self-belief, self-esteem, self-acceptance, self-love, self-renewal: how would you like to transform your "self"? Self-help guru Brian Mayne guides readers on an inward journey of personal discovery that will lead them to greater fulfillment and confidence. The results are amazing, yet his method is beautifully simple: Mayne explains how to create a "Self Map" that activates the whole brain and serves as a reminder of the person you'd like to become. This map literally acts like a subconscious command, and stimulates you to act upon your desires; by regularly meditating on it you can integrate your "low-self ego" with your "High-Self spirit" to become your authentic, natural True Self. - Mayne's earlier book,Goal Mapping(Duncan Baird, 2006), has become an international success with multiple printings and translations into several foreign languages - This book has the power to transform lives by showing you how to integrate thoughts, feelings, and actions and become your True Self - Because of their simplicity, depth, and effectiveness, self-mapping workshops are popular with both the general public as well as with large multinationalsincluding Microsoft and Siemens

Created by the hugely successful teacher Brian Mayne, Self Mapping uses a combination of words and images to create a Self Map. Connecting the left brain and the right brain, this unique technique will help anyone to discover their true potential. Self Mapping will take you on a journey of Self discovery, through the stages of Self Awareness, Self Belief, Self Image, Self Esteem, Self Acceptance, Self Love, Self Actualization, Self Integration and Self Renewal. Each of these stages are covered in individual chapters, and steps for creating your Self Map are given at the end of each chapter. The system is designed to help you become the best you, in outlook, attitude and actions. As you work through the book you will create your own Self Map, using the template provided along with a combination of words and images. Creating your Self Map can happen in a single hour-long session, or be spread over many sessions and several days. Once completed it serves both as a conscious reminder about who you choose to become and a subconscious command to make it happen. By regularly meditating on your Self Map you can integrate your Low-self ego with your High-Self spirit to become your True Self; your natural and authentic self, or to put it simply, be yourself.

Since the early twentieth-century, Kurds have challenged the borders and national identities of the states they inhabit. Nowhere is this more evident than in their promotion of the 'Map of Greater Kurdistan', an ideal of a unified Kurdish homeland in an ethnically and geographically complex region. This powerful image is embedded in the consciousness of the Kurdish people, both within the region and, perhaps even more strongly, in the diaspora. Addressing the lack of rigorous research and analysis of Kurdish politics from an international perspective, Zeynep Kaya focuses on self-determination, territorial identity and international norms to suggest how these imaginations of homelands have been socially, politically and historically constructed (much like the state territories the Kurds inhabit), as opposed to their perception of being natural, perennial or intrinsic. Adopting a non-political approach to notions of nationhood and territoriality, Mapping Kurdistan is a systematic examination of the international processes that have enabled a wide range of actors to imagine and create the cartographic image of greater Kurdistan that is in use today.

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