The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

A key question in the design of specialized hardware for simulation of neural networks is
whether fixedpoint arithmetic of limited numerical precision can be used with existing …

During the past 10 years the fields of artificial neural networks (ANNs) and massively
parallel computing have been evolving rapidly. In this paper we study the attempts to make …

This paper describes elements necessary for a general-purpose low-cost very large scale
integration (VLSI) neural network. By choosing a learning algorithm that is tolerant of analog …

A key question in the design of specialized hardware for simulation of neural networks is
whether fixed-point arithmetic of limited precision can be used with existing learning …

This paper describes concepts that optimize an on-chip learning algorithm for
implementation of VLSI neural networks with conventional technologies. The network …

This section considers some of the alternative approaches towards modeling biological
functions by digital circuits. It starts by introducing some circuit complexity issues and …

We consider multilayer neural network models which are applied to stochastic classification
problems and are trained with error back propagation methods. Expectations for network …

Mitchell's method typically is used to compute approximate base-2 antilogarithms using
minimal hardware (no ROM). Another use of identical hardware approximates the function …

This section considers some of the alternative approaches towards modeling biological
functions by digital circuits. It starts by introducing some circuit complexity issues and …