The recent resurgence of interest in neural networks has its roots in the recognition that the brain performs computations in a different manner than do conventional digital computers. Computers are extremely fast and precise at executing sequences of instructions that have been formulated for them. A human information processing system is composed of neurons switching at speeds about a million times slower than computer gates. Yet, humans are more efficient than computers at computationally complex tasks such as speech understanding. Moreover, not only humans, but even animals, can process visual information better than the fastest computers.
The question of whether technology can benefit from emulating the computational capabilities of organisms is a natural one. Unfortunately, the understanding of biological neural systems is not developed enough to address the issues of functional similarity that may exist between the biological and man-made neural systems. As a result, any major potential gains derived from such functional similarity, if they exist, have yet to be exploited.
This book introduces the foundations of artificial neural systems. Much of the inspiration for such systems comes from neuroscience. However, we are not directly concerned with networks of biological neurons in this text. Although the newly developed paradigms of artificial neural networks have strongly contributed to the discovery, understanding, and utilization of potential functional similarities between human and artificial information processing systems, many questions remain open. Intense research interest persists and the area continues to develop. The ultimate research objective is the theory and implementation of massively parallel interconnected systems which could process the information with an efficiency comparable to that of the brain.