An integrated network for invariant visual detection and recognition

We describe an architecture for invariant visual detection and recognition. Learning is performed in a single central module. The architecture makes use of a replica module consisting of copies of retinotopic layers of local features, with a particular design of inputs and outputs, that allows them to be primed either to attend to a particular location, or to attend to a particular object representation. In the former case the data at a selected location can be classified in the central module. In the latter case all instances of the selected object are detected in the field of view. The architecture is used to explain a number of psychophysical and physiological observations: object based attention, the different response time slopes of target detection among distractors, and observed attentional modulation of neuronal responses. We hypothesize that the organization of visual cortex in columns of neurons responding to the same feature at the same location may provide the copying architecture needed for translation invariance.