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Correlative Learning: A Basis for Brain and Adaptive Systems: 49 (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control) - Tapa dura

Libro 14 de 33: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

Chen, Zhe

 
9780470044889: Correlative Learning: A Basis for Brain and Adaptive Systems: 49 (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)
Tapa dura
ISBN 10: 0470044888 ISBN 13: 9780470044889
Editorial: Wiley-Interscience, 2007

Sinopsis

Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers. The book also presents an overview of the role of correlation in the human brain as well as in the adaptive signal processing world; unifies many well-established synaptic adaptations (learning) rules within the correlation-based learning framework, focusing on a particular correlative learning paradigm, ALOPEX; and presents case studies that illustrate how to use different computational tools and ALOPEX to help readers understand certain brain functions or fit specific engineering applications.

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Acerca del autor

Zhe Chen, PhD, is currently a Research Fellow in the Neuroscience Statistics Research Laboratory at Harvard Medical School.

Simon Haykin, PhD, DSc, is a Distinguished University Professor in the Department of Electrical and Computer Engineering at McMaster University, Ontario, Canada.

Jos J. Eggermont, PhD, is a Professor in the Departments of Physiology & Biophysics and Psychology at the University of Calgary, Alberta, Canada.

Suzanna Becker, PhD, is a Professor in the Department of Psychology, Neuroscience, and Behavior at McMaster University, Ontario, Canada.

De la contraportada

This book bridges the communication gap between neuroscientists and engineers through the unifying theme of correlation-based learning

Developing brain-style signal processing or machine learning algorithms has attracted many sharp minds from a range of disciplines. Now, coauthored by four researchers with varying backgrounds in signal processing, neuroscience, psychology, and computer science, Correlative Learning unifies the many cross-fertilized ideas in computational neuroscience and signal processing in a common language that will help engineers understand and appreciate the human brain as a highly sophisticated biosystem for building more intelligent machines.

First, the authors present the necessary neuroscience background for engineers, and then go on to relate the common intrinsic structures of the learning mechanisms of the brain to signal processing, machine learning, kernel learning, complex-valued domains, and the ALOPEX learning paradigm.

This correlation-based approach to building complex, reliable (robust), and adaptive systems is vital for engineers, researchers, and graduate students from various fields of science and engineering. Figures, tables, worked examples, and case studies illustrate how to use computational tools for either helping to understand brain functions or fitting specific engineering applications, and a comprehensive bibliography covering over 1,000 references from major publications is included for further reading.

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Editorial
Wiley-Interscience
Año de publicación
2007
Idioma
Inglés
ISBN 10 
0470044888
ISBN 13 
9780470044889
Encuadernación
Tapa dura
Número de páginas
476
Contacto del fabricante
no disponible
Persona responsable
Wiley-VCH GmbH
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