Collaborative Research: High Performance Neural Computing

合作研究：高性能神经计算

• 批准号: 0749157
• 负责人: Steven Zucker
• 金额: $ 13万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0749157&HistoricalAwards=false
• 关键词: Collaborative; Research; Performance; Neural; Computing

The investigators propose to a) develop tools for electrophysiologically realistic simulations of large areas of mammalian cortex using modern computers with many thousands of (hetero- or homogeneous) processors b) use genetic programming techniques to evolve models of primary and secondary visual cortical areas to solve difficult image processing tasks, namely image segmentation, c)understand the structure of computations performed by the brain (that is, its computational primitives) and discover the level of biological detail necessary and sufficient for these computations.A distinguishing trait of the proposed approach is that physiological realism is not the goal, and it will be attempted only to the extent that it is needed for understanding the neural computation and for solving complex information processing tasks. That is, functional performance will be the means of bridging over gaps in the existing knowledge. Thus the resulting cortical models fall between the traditional (and oversimplified) Artificial Neural Networks and biomedically-inspired cellular and molecular descriptions.

研究人员建议：a）开发工具，利用具有数千个神经元的现代计算机，对哺乳动物大脑皮层的大面积进行电生理学逼真的模拟。（异质或同质）处理器B）使用遗传编程技术来进化初级和次级视觉皮层区域的模型，以解决困难的图像处理任务，即图像分割，c）理解由大脑执行的计算的结构所提出的方法的一个显着特点是，生理现实主义不是目标，它将尝试只在一定程度上，它是需要理解的神经计算和解决复杂的信息处理任务。也就是说，职能绩效将是弥补现有知识差距的手段。因此，由此产生的皮质模型介于传统（和过度简化）的人工神经网络和生物医学启发的细胞和分子描述之间。