Transform Invariant Recognition by Association in a Recurrent Network

Objects can be recognized independently of the view they present, of their position on the retina, or their scale. It has been suggested that one basic mechanism that makes this possible is a memory effect, or a trace, that allows associations to be made between consecutive views of one object. In this work, we explore the possibility that this memory trace is provided by the sustained activity of neurons in layers of the visual pathway produced by an extensive recurrent connectivity. We describe a model that contains this high recurrent connectivity and synaptic efficacies built with contributions from associations between pairs of views that is simple enough to be treated analytically. The main result is that there is a change of behavior as the strength of the association between views of the same object, relative to the association within each view of an object, increases. When its value is small, sustained activity in the network is produced by the views themselves. As it increases above a threshold value, the network always reaches a particular state (which represents the object) independent of the particular view that was seen as a stimulus. In this regime, the network can still store an extensive number of objects, each defined by a finite (although it can be large) number of views.