Autoassociative memory with adjustable behavior

Summary form only given, as follows. An autoassociative high-capacity neural network memory architecture based on a competitive learning mechanism is described. This network uses a modified version of ART 1 dynamics and inherits some of its desirable properties. It has a tunable error-correction capability and well behaved basins of attraction, which are independent of the size of the network. The dynamic adjustment of stored vectors according to the idiosyncracies of a particular input environment is another notable feature of this architecture, especially in the absence of a complete knowledge of the nature of the input environment.<>