Error recovery behaviour of feedback RAM-networks

Presents an analysis of the dynamics involved in the process of error recovery in Boolean neural nets with feedback loops. The main objective of the work is to show the conditions under which a randomly generated RAM-network recovers from input and state errors. RAM-nets of the type mentioned tend to have some inherent stability with respect to its input sequence. The approach adopted here is a probabilistic one based on the theory of discrete-time stationary Markov chains though the net is in fact deterministic. The reason for adopting such approach is due to the fact that the RAM nodes connections and logical functions are randomly selected in order to make the analysis as general as possible. Also, besides the vast number of states the net can enter, both the encoding of the input patterns and the initial state of the net are chosen at random