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Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation & those employing symbolic computation can strengthen one another. This work set develops a cognitive architecture based in neural computation, but supports formally explicit higher-level symbolic descriptions.

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...

Despite their apparently divergent accounts of higher cognition, cognitive theories based on neural computation & those employing symbolic computation can strengthen one another. This work set develops a cognitive architecture based in neural computation, but supports formally explicit higher-level symbolic descriptions.

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.