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Correlative Learning
A Basis for Brain and Adaptive Systems
Langbeschreibung
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.
Zhe Chen, PhD, is currently a Research Fellow in theNeuroscience Statistics Research Laboratory at Harvard MedicalSchool.Simon Haykin, PhD, DSc, is a Distinguished University Professorin the Department of Electrical and Computer Engineering atMcMaster University, Ontario, Canada.Jos J. Eggermont, PhD, is a Professor in the Departments ofPhysiology & Biophysics and Psychology at the University ofCalgary, Alberta, Canada.Suzanna Becker, PhD, is a Professor in the Department ofPsychology, Neuroscience, and Behavior at McMaster University,Ontario, Canada.
ISBN-13:
9780470171448
Veröffentl:
2008
Seiten:
480
Autor:
Zhe Chen
Serie:
1, Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control
eBook Typ:
PDF
eBook Format:
Reflowable
Kopierschutz:
2 - DRM Adobe
Sprache:
Englisch
158,99 €*
Lieferzeit: Sofort lieferbar
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