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.
A propos de l’auteur
Zhe Chen, Ph D, is currently a Research Fellow in the
Neuroscience Statistics Research Laboratory at Harvard Medical
School.
Simon Haykin, Ph D, DSc, is a Distinguished University Professor
in the Department of Electrical and Computer Engineering at
Mc Master University, Ontario, Canada.
Jos J. Eggermont, Ph D, is a Professor in the Departments of
Physiology & Biophysics and Psychology at the University of
Calgary, Alberta, Canada.
Suzanna Becker, Ph D, is a Professor in the Department of
Psychology, Neuroscience, and Behavior at Mc Master University,
Ontario, Canada.
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