A Large Scale Neural Network and Its Applications

Neural Networks have been developed since the 1940's [1] in order to model and to grossly simulate the biological central nervous system (CNS) on the one hand, and in order to develop computational tools that can take advantage of the remarkable computational capabilities and the efficiency of the CNS on the other hand. When observing that a simple house-fly, with only a few hundred neural cells, with signal propagation speeds averaging 3 meters/second and with bit rates of the order of 100 Hz can compute flight trajectories to evade a human hand trying to catch the fly, then one can understand the potential involved in imitating the biological computation system. This computational ability is achieved even while the average house-fly probably holds no Ph.D. in mathematics or in computer science. Indeed, the biological neural network is strikingly efficient in its recognition and retrieval capabilities. Its abilities of generalization, and of dealing with non-analytical and incomplete while huge data bases, within a virtually fixed architecture that involves no reprogramming when moving from one class of tasks to another, is well beyond those of any other computational architecture. The latter computational tasks involving huge data bases with partly missing data sets and where data is in part non-analytical and/or fuzzy and/or stochastic, are the main challenges for today's computer science. This is indeed the motivation for presenting the large-scale neural network of the present article.