E-raamat: Correlative Learning: A Basis for Brain and Adaptive Systems

Developing brain-style signal processing or machine learning algorithms has been the Holy Grail of artificial intelligence, notes Chen (Neuroscience Statistics Research Laboratory, Harvard Medical School; RIKEN Brain Science Institute, Tokyo, Japan) in introducing eight chapters by international multidisciplinary researchers. In examining generalized concepts of correlation underlying both neural networks and complex engineering systems, they discuss the key role of correlation in normal and abnormal neural functions; correlation-based mathematical learning paradigms such as ALOPEX (Algorithm Of Pattern EXtraction); and case studies (e.g., of online training of artificial neural networks). Appendices include further details on correlation, stochastic learning rules, methods for estimating various functions and parameters, and a primer on linear algebra. The bibliography lists 1,002 references. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

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.