This is a textbook for courses commonly called neural networks in departments of computer and information science. This unique neural network book will describe novel architectures and learning mechanisms of model-based neural networks that utilize and intriguing concept of an internal "world" model. This concept combines a prior knowledge of models with adaptive learning and addresses the most perplexing problems in the fields of neural networks: fast learning and robust generalization. The author provides an overview of neural networks and artificial intelligence fields, relating hundreds of seemingly disparate techniques to several basic mathematical concepts. He then analyzes fundamental computational concepts of major neural network paradigms, and relates them to concepts of mind in philosophy, pschology, and linguistics. Relationships of these mathematical concepts to the concepts of philosophy will help students and researchers determine the directions of future research. This book can also be used as a supplementary text in a graduate course on Neural Networks.

A diverse international set of authors discuss Artificial/Computational Sapience and Sapient Systems in this unique and useful volume. The reader is guided through the subject in a structured and comprehensive manner that begins with chapters discussing philosophical, historical, and semiotic ideas about what properties are expected from Sapient (Wise) systems. Following that, chapters describe mathematical and engineering views on sapience, relating these to philosophical, semiotic, cognitive, and neuro-biological perspectives.

Dynamic logic (DL) recently had a highest impact on the development in several areas of modeling and algorithm design. The book discusses classical algorithms used for 30 to 50 years (where improvements are often measured by signal-to-clutter ratio), and also new areas, which did not previously exist. These achievements were recognized by National and International awards. Emerging areas include cognitive, emotional, intelligent systems, data mining, modeling of the mind, higher cognitive functions, evolution of languages and other. Classical areas include detection, recognition, tracking, fusion, prediction, inverse scattering, and financial prediction. All these classical areas are extended to using mixture models, which previously was considered unsolvable in most cases. Recent neuroimaging experiments proved that the brain-mind actually uses DL. Emotional Cognitive Neural Algorithms with Engineering Applications" is written for professional scientists and engineers developing computer and information systems, for professors teaching modeling and algorithms, and for students working on Masters and Ph.D. degrees in these areas. The book will be of interest to psychologists and neuroscientists interested in mathematical models of the brain and min das well.

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Dynamic logic (DL) recently had a highest impact on the development in several areas of modeling and algorithm design. The book discusses classical algorithms used for 30 to 50 years (where improvements are often measured by signal-to-clutter ratio), and also new areas, which did not previously exist. These achievements were recognized by National and International awards. Emerging areas include cognitive, emotional, intelligent systems, data mining, modeling of the mind, higher cognitive functions, evolution of languages and other. Classical areas include detection, recognition, tracking, fusion, prediction, inverse scattering, and financial prediction. All these classical areas are extended to using mixture models, which previously was considered unsolvable in most cases. Recent neuroimaging experiments proved that the brain-mind actually uses DL. "Emotional Cognitive Neural Algorithms with Engineering Applications" is written for professional scientists and engineers developing computer and information systems, for professors teaching modeling and algorithms, and for students working on Masters and Ph.D. degrees in these areas. The book will be of interest to psychologists and neuroscientists interested in mathematical models of the brain and min das well.

A diverse international set of authors discuss Artificial/Computational Sapience and Sapient Systems in this unique and useful volume. The reader is guided through the subject in a structured and comprehensive manner that begins with chapters discussing philosophical, historical, and semiotic ideas about what properties are expected from Sapient (Wise) systems. Following that, chapters describe mathematical and engineering views on sapience, relating these to philosophical, semiotic, cognitive, and neuro-biological perspectives.

Music, Passion, and Cognitive Function examines contemporary cognitive theories of music, why they cannot explain music's power over us, and the origin and evolution of music. The book presents experimental confirmations of the theory in psychological and neuroimaging research, discussing the parallel evolution of consciousness, musical styles, and cultures since Homer and King David. In addition, it explains that 'in much wisdom is much grief' due to cognitive dissonances created by language that splits the inner world. Music enables us to survive in this sea of grief, overcomes discomforts and stresses of acquiring new knowledge, and unifies the soul, hence the power of music.