BioCOMP: Biologically Inspired Computational Model for Perception

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Abstract: The University of California Riverside is awarded a grant to develop biologically inspired computational models for visual perception. Perceptual systems take raw sensory input like digital photographs/movies and identify the objects they contain. Decades have been spent trying to develop perceptual systems, with only modest success. The key innovation of this research is that it incorporates the biological constraints neuroscientists have identified that guide the developmental of natural perceptual systems into the automated development of computational perceptual systems. The project involves an interdisciplinary team and a close collaboration between a computer scientist and a cognitive psychologist. The systems developed in this research learn to work in a way that is similar to biological systems. The project develops a new paradigm for incorporating domain-specific knowledge in this case, biological constraints into evolutionary computation to develop innovative visual systems. This approach systematically addresses the complexity and magnitude of the object detection/recognition problem in real-world environments. The research generates computational innovations to permit the development of evolutionary learning systems that can utilize developmental neuropsychological constraints. These include (a) cooperative coevolution that allows components of a task to evolve in an environment in which cooperation improves fitness, (b) smart crossover and mutation operators that retain effective components over generations of computational evolution, and (c) a minimum description length constraint that selects operators based on efficiency of description in addition to goodness of fit. The goal of these innovations, collectively and individually, is to reduce the volume of the search space that the evolutionary learning process must traverse to allow it to solve the perceptual problem. The project will use several publicly available databases to demonstrate the results.