How can a computing system as complex as the human visual system be specified and constructed? Recent discoveries of widespread spontaneous neural activity suggest a simple yet powerful explanation: genetic information may be expressed as internally generated training patterns for a general-purpose learning system. The thesis presents an implementation of this idea as a detailed, large-scale computational model of visual system development. Simulations show how newborn orientation processing and face detection can be specified in terms of training patterns, and how postnatal learning can extend these capabilities. The results explain experimental data from laboratory animals, human newborns, and older infants, and provide concrete predictions about infant behavior and neural activity for future experiments. They also suggest that combining a pattern generator with a learning algorithm is an efficient way to develop a complex adaptive system.

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Ph.D. Thesis; Department of Computer Sciences, The University of Texas at Austin. Technical Report~AI-TR-02-294, May 2002