Correlative Learning - Simon Haykin

Correlative Learning by Simon Haykin offers a comprehensive overview of the relationship between three important fields: computational neuroscience, neural network design, and signal processing techniques. Starting with a brief introduction to neuroscience fundamentals, the book goes on to discuss the role of correlations in both the human brain and the world of adaptive signal processing. The authors present a unified framework for understanding many established synaptic adaptation (learning) mechanisms within the context of correlative learning, focusing on the ALOPEX paradigm. Finally, the book offers case studies to illustrate how various computational tools and the ALOPEX approach can be used to better understand certain aspects of brain function or to solve specific engineering problems.

Электронная Книга «Correlative Learning» написана автором Simon Haykin в году.

Минимальный возраст читателя: 0

Язык: Английский

ISBN: 9780470171448

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.