This thesis presents an innovative approach to pattern recognition, by using self-organized, invariant representations integrating continuous observation and saccade movements. This biologically motivated approach can achieve visual perception through a retina like sampling of high resolution images with lower resolution artificial retina.
The neural network uses hierarchical feedback structures to build object representations, self-organizes invariant transformations, while iterates on the images received from the retina model. The network identifies the whole image by using winner-take-all scheme through temporal association of sufficiently accurate saccades. By using our invariance building scheme, the network can identify different views of the same object.