This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this...
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing t...
In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation.
This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field....
In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through p...
