A discrete version of the dynamic link network

Learning in a dynamic link network (DLN) is a composition of two dynamics: neural dynamics inside layers and link dynamics between layers. Based upon a rigorous analysis of the neural dynamics, we find an algorithm for selecting the parameters of the DLN in such a way that the neural dynamics preferentially converges to any chosen attractor. This control is important because the attractors of the neural dynamics determine the link dynamics which is the main tool for pattern retrieval. Thus in terms of our constructive algorithm it is possible to explore the link dynamics using all kinds of attractors of the neural dynamics, In particular, we show how to get on-center activity patterns which have been extensively used in the application of the DLN to image recognition tasks as well as having an important role in the image processing of the retina. We propose also a Hopfield-like discretized version of the neural dynamics which converges to the attractors much faster than the original DLN.