B. Müller, J. Reinhardt
The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach. After a brief overview of the neural structure of the brain and the history of neural-network modelling, the reader is introduced to "neural" information processing, such as associative memory, perceptrons, feature-sensitive networks, learning strategies and practical applications. Part 2 covers more advanced subjects such as spin glasses, the mean-field theory of the Hopfield model, and the space of interactions in neural networks. The self-contained final part discusses seven programmes that provide practical demonstrations of neural-network models and their learning strategies. Software is included with the text on a 5 1/4-inch MS-DOS diskette and can be run using Borland's TURBO-C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers.