Silicon Implementation of Pulse Coded Neural Networks (Paperback, Softcover reprint of the original 1st ed. 1994)

When confronted with the how's and why's of nature's computational engines, some prefer to focus upon neural function: addressing issues of neural system behavior and its relation to natural intelligence. Then there are those who prefer the study of the 'mechanics' of neural systems: the nuts and bolts of the 'wetware': the neurons and synapses. Those who investigate pulse coded implementations of artificial neural networks know what it means to stand at the boundary which lies between these two worlds: not just asking why natural neural systems behave as they do, but also how they achieve their marvelous feats. The state-of-the-art research results presented in Silicon Implementation of Pulse Coded Neural Networks not only address more conventional abstract notions of neural-like processing, but also the more specific details of neural-like processors. It has been established for some time that natural neural systems perform a great deal of information processing via electrochemical pulses. Accordingly, pulse coded neural network concepts are receiving increased attention in artificial neural network research.This increased interest is compounded by continuing advances in the field of VLSI circuit design. For the first time in history, it is practical to construct networks of neuron-like circuits of reasonable complexity that can be applied to real problems. The pioneering work in artificial neural systems presented in Silicon Implementation of Pulse Coded Neural Networks will lead to further advances that will not only be useful in some practical sense, but may also provide some additional insight into the operation of their natural counterparts. Silicon Implementation of Pulse Coded Neural Networks seeks to cover many of the relevant contemporary studies coming out of this newly emerging area. As such, it serves as an excellent reference, and may be used as a text for advanced courses on the subject.