Systematically explores the relationship between principal component analysis (PCA) and neural networks Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks Using a unified formulation, the authors present neural models performing PCA from оеэдн the Hebbian learning rule and those which use least squares learning rules such as back-propagation Examines the principles of biological perceptual systems to explain how the brain works Every chapter contains a selected list of applications examples from diverse areas.  granatuISBN 0471054364.