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This collection of papers covers research in the field of neural networks. Although summer school has always leaned towards cognitive science and artificial intelligence, the diverse backgrounds of participants reflects the broad spectrum of areas contributing to neural networks.

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian...

A body of results of new mathematical learning and processing in neural networks. The book also fills in background in such areas as computability theory, theory of analogue computation, time-series analysis, Bayesian analysis, computational learning theory and mathematical statistics.