BioCOMP: Biologically Inspired Computational Model for Perception

翻译后摘要：加州大学滨江被授予赠款开发生物启发的计算模型的视觉感知。感知系统获取原始的感官输入，如数字照片/电影，并识别它们包含的对象。 几十年来，人们一直试图开发感知系统，但只取得了有限的成功。 这项研究的关键创新之处在于，它结合了神经科学家已经确定的生物学约束，这些生物学约束引导自然感知系统的发展进入计算感知系统的自动化发展。 该项目涉及一个跨学科的团队，以及计算机科学家和认知心理学家之间的密切合作。在这项研究中开发的系统学习以类似于生物系统的方式工作。 该项目开发了一种新的范式，用于将特定领域的知识（在这种情况下，生物约束）纳入进化计算，以开发创新的视觉系统。这种方法系统地解决了在现实世界的环境中的对象检测/识别问题的复杂性和规模。这项研究产生了计算创新，允许开发可以利用发展神经心理学约束的进化学习系统。这些措施包括（a）合作协同进化，允许一个任务的组成部分，在合作提高适应度的环境中发展，（B）智能交叉和变异算子，保留有效的组件在几代的计算进化，以及（c）最小描述长度约束，选择运营商的基础上，除了拟合优度的描述效率。这些创新的目标，无论是共同的还是单独的，都是为了减少进化学习过程必须遍历的搜索空间的体积，以使其能够解决感知问题。该项目将使用几个公开的数据库来展示结果。

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.