ABSTRACT
This section considers some of the alternative approaches towards modeling biological functions by digital circuits. It starts by introducing some circuit complexity issues and arguing that there is considerable computational and physiological justification that shallow threshold gate circuits are computationally more efficient than classical Boolean circuits. We comment on the tradeoff between the depth and the size of a threshold gate circuit, and on how design parameters like fan-in, weights and thresholds influence the overall area and time performances of a digital neural chip. This is followed by briefly discussing the constraints imposed by digital technologies and by detailing several possible classification schemes as well as the performance evaluation of such neurochips and neurocomputers. Lastly, we present many typical and recent examples of implementation and mention the ‘VLSI-friendly learning algorithms’ as a promising direction of research.
