Associative recall based on abstract object descriptions learned from observation: the CBM neural net model

The associative recall problem is treated by means of a novel neural net model. The classifier-based model (CBM) uses internal representations of objects automatically developed from observation. A feedback loop consisting of a Coulomb network is introduced into a classifier network. The classifier learns descriptions of various object types from observation of typical instances. The internal representations of the classifier are used by the Coulomb network to transform an incomplete or noisy input dynamically. Since the recall is based on abstract object descriptions, the output is not limited to an a priori specified collection of memories (objects). Results obtained for a simulation of the model show that it will retrieve a memory which satisfies the classification requirement and is compatible with the cue. The model can store any number of memories without degrading the quality of the memory retrieved. Either a schema or a distinct memory can be retrieved. The classifier prunes irrelevant objects from the object space, promoting faster convergence to an appropriate memory