Recognizing image invariants in a neural network architecture

A description is given of an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are used to perform indexing of the appropriate object-centered model from image-centered primitives. The system consists of three network modules, each of which represents information relative to a different frame of reference. The model memory network is a large state-space vector, where fields in the vector correspond to ordered component objects and relative, object-based spatial relationships between the component objects. The component assertion network represent evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image-based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object-based and the image-based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image-based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above.<>