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Self-organized maps have been proposed as a model for the formation of sensory maps in the cerebral cortex. The role of inputs is critical in this process of self-organization. This paper presents a systematic approach to analyzing the relationship between the input ensemble and the quality of self-organization achieved. We present a method for generating an input stimulus set consisting of images of curved lines. The advantage of this approach is that it allows the user the ability to precisely control the statistics of the input stimuli to visual processing algorithms. Since there is considerable scientific interest in the processing of information in the human visual stream, we specifically address the problem of self-organization of cortical visual areas V1 and V2. We show that the statistics of the curves generated with our algorithm match the statistics of natural images. We develop a measure of self-organization based on the oriented energy contained in the afferent weights to each cortical unit in the map. We show that as the curvature of the generated lines increases, this measure of self-organization decreases. Furthermore, self-organization using curved lines as stimuli is achieved much more rapidly, as the curve images do not contain as much higher order structure as natural images do.

Evaluation of the Effect of Input Stimuli on the Quality of Orientation Maps Produced Through Self Organization