We survey and summarize the literature on the computational aspects of neural network
models by presenting a detailed taxonomy of the various models according to their …

INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …

We pursue a particular approach to analog computation, based on dynamical systems of the
type used in neural networks research. Our systems have a fixed structure, invariant in time …

It is shown that high order feedforward neural nets of constant depth with piecewise
polynomial activation functions and arbitrary real weights can be simulated for boolean …

In the past decade, there has been growing interest in neural networks and their
applications in such disciplines as neurobiology, computer science, engineering …

For any research field to have a lasting impact, there must be a firm theoretical foundation.
Neural networks research is no exception. Some of the founda tional concepts, established …

Neural networks usually work adequately on small problems but can run into trouble when
they are scaled up to problems involving large amounts of input data. Circuit Complexity and …

In this paper, we address the capabilities and cost-effectiveness of the current models of
neural networks for carrying out general computation. We show that neural networks are …

Recent years have seen an explosion of new mathematical results on learning and
processing in neural networks. This body of results rests on a breadth of mathematical …

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 …