A new model of associative memories network

An associative memory (AM) is a special kind of neural network that only allows associating an output pattern with an input pattern. However, some problems require associating several output patterns with a unique input pattern. Classical associative and neural models cannot solve this simple task. In this paper we propose a new network composed of several AMs aimed to solve this problem. By using this new model, AMs can be able to associate several output patterns with a unique input pattern. We test the accuracy of the proposal with a database of real images. We split this database of images into four collections of images and then we trained the network of AMs. During training we associate an image of a collection with the rest of the images belonging to the same collection. Once trained the network we expected to recover a collection of images by using as an input pattern any image belonging to the collection.