Recurrent neural network architectures: An overview -- Gradient based learning methods -- Diagrammatic methods for deriving and relating temporal neural network algorithms -- An introduction to learning structured information -- Neural networks for processing data structures -- The loading problem: Topics in complexity -- Learning dynamic Bayesian networks -- Probabilistic models of neuronal spike trains -- Temporal models in blind source separation -- Recursive neural networks and automata -- The neural network pushdown automaton: Architecture, dynamics and training -- Neural dynamics with stochasticity -- Parsing the stream of time: The value of event-based segmentation in a complex real-world control problem -- Hybrid HMM/ANN systems for speech recognition: Overview and new research directions -- Predictive models for sequence modelling, application to speech and character recognition.

This book is devoted to adaptive processing of structured information similar to flexible and intelligent information processing by humans - in contrast to merely sequential processing of predominantly symbolic information within a deterministic framework. Adaptive information processing allows for a mixture of sequential and parallel processing of symbolic as well as subsymbolic information within deterministic and probabilistic frameworks. The book originates from a summer school held in September 1997 and thus is ideally suited for advanced courses on adaptive information processing and advanced learning techniques or for self-instruction. Research and design professionals active in the area of neural information processing will find it a valuable state-of-the-art survey.

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