Qualitative limitations incurred in implementations of recurrent neural networks

During the implementation process of artificial neural networks, deviations from the desired ideal neural network are frequently introduced. These include parameter perturbations, transmission delays, and interconnection constraints. In the present article, we study the effects of these realities of imperfection on the qualitative behavior of artificial feedback neural networks. To accomplish this, we utilize a specific class of neural networks (Hopfield-like neural networks) with a specific application (the realization of associative memories) as a vehicle for our study. The principal issues which we address concern the effects of parameter perturbations, transmission delays, and interconnection constraints on the accuracy and on the qualitative properties of the network memories