This collection of papers is an up-to-date introduction to the field of neural networks. It covers neural network algorithms, neural architectures, applications in speech and image processing and models in neurobiology.

Inhalt
1 Algorithms.- Incorporating knowledge in multi-layer networks: the example of protein secondary structure prediction.- Product units with trainable exponents and multi-layer networks.- Recurrent backpropagation and Hopfield networks.- Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear case.- Single-layer learning revisited: a stepwise procedure for building and training a neural network.- Synchronous Boltzmann machines and Gibbs fields: learning algorithms.- Fast computation of Kohonen self-organization.- Learning algorithms in neural networks: recent results.- Statistical approach to the Jutten-Herault algorithm.- The N programming language.- Neural networks dynamics.- Dynamical analysis of classifier systems.- Neuro-computing aspects in motor planning and control.- Neural networks and symbolic A.I.- 2 Architectures.- Integrated artificial neural networks: components for higher level architectures with new properties.- Basic VLSI circuits for neural networks.- An analog VLSI architecture for large neural networks.- Analog implementation of a permanent unsupervised learning algorithm.- An analog cell for VLSI implementation of neural networks.- Use of pulse rate and width modulations in a mixed analog/digital cell for artificial neural systems.- Parallel implementation of a multi-layer perceptron.- A monolithic processor array for stochastic relaxation using optical random number generation.- Dedicated neural network: a retina for edge detection.- Neural network applications in the Edinburgh concurrent supercomputer project.- The semi-parallel architectures of neuro computers.- 3 Speech.- Speech coding with multilayer networks.- Statistical inference in multilayer perceptrons and hidden Markov models with applications in continuous speech recognition.- Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition.- Data compression using multilayer perceptrons.- Guided propagation: current state of theory and applications.- Speaker adaptation using multi-layer feed-forward automata and canonical correlation analysis.- Analysis of linear predictive data as speech and of ARM A processes by a class of single-layer connectionist models.- High and low level speech processing by competitive neural networks: from psychology to simulation.- Connected word recognition using neural networks.- 4 Image.- Handwritten digit recognition: applications of neural net chips and automatic learning.- A method to de-alias the scatterometer wind field: a real world application.- Detection of microcalcifications in mammographie images.- What is a feature, that it may define a character, and a character, that it may be defined by a feature?.- A study of image compression with backpropagation.- Distortion invariant image recognition by Madaline and back-propagation learning multi-networks.- An algorithm for optical flow.- 5 Neuro-biology.- Multicellular processing units for neural networks: model of columns in the cerebral cortex.- A potentially powerful connectionist unit: the cortical column.- Complex information processing in real neurons.- Formal approach and neural network simulation of the co-ordination between posture and movement.- Cheapmonkey: comparing an ANN and the primate brain on a simple perceptual task: orientation discrimination.- References.- List of contributors.