A self-learning map-seeking circuit for visual object recognition

The mammalian visual system is uniquely capable of robustly recognizing objects in its field of view regardless of their orientation, scale, or position, while learning new objects from a small number of training examples and generalizing robustly to a broad class of visually similar objects. The cortical structures that implement the visual system have been successfully emulated in several biologically-inspired synthetic vision systems. Developmental evidence lends credence to the claim that visual cortical structures emerge during development, i.e. they self-organize, when exposed to training stimulus. This paper demonstrates that a set of simple developmental rules can govern the emergence of a self-learning variant of a map-seeking circuit (SL-MSC) in a simulated visual system. The SL-MSC is capable of the same visual tasks as the original hand-crafted MSC: object recognition independent of rotation, translation, and scaling, and the ability to identify and learn new objects. The SL-MSC learns invariant visual transformations by relying on temporal association in its visual field, and is able to group the transformations into independent layers. Experimental results show that the SL-MSC can generalize the rotation, translation, and scaling transformations learned for one object to new objects, leading to learning and recognition of new objects with very few training samples.