BAC: Analog-VLSI and Parallel-Computer Modeling of Intersegmental Coordination

BAC：部门间协调的模拟 VLSI 和并行计算机建模

• 批准号: 9511721
• 负责人: Stephen DeWeerth
• 金额: $ 32.47万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-01 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9511721&HistoricalAwards=false
• 关键词: BAC; Analog; VLSI; Parallel; Computer

9511721 DeWeerth Humans and animals move around in their environments with seeming ease. To design better robotic devices, engineers would like to capture the design principles that animals use. This in an interdisciplinary project between engineers, computer scientists and biologist directed at gaining a better understanding of the ways animals control movement, by studying the neural systems that generate swimming motion in fish and other simple animals. This research will consist both of studying the nervous system of the particular animals and of modeling the behaviors generated by these systems. The circuits in the nervous systems that create the observed patterns of movement, even in these relatively simple animals are very complex. Therefore, the research of these investigators will focus on the design of very large-scale integrated (VLSI) circuits that emulate the function of the biology. These complex artificial circuits make it much easier to create neuron firing patterns in real time that are very similar to those found in their biological counterparts. The end goal of this research is to develop a better understanding of the biology while learning how to better engineer systems that perform complex tasks that can currently only be performed by humans and other animals.

9511721名德威特人和动物在他们的环境中似乎很轻松地移动。为了设计更好的机器人设备，工程师们希望捕捉动物使用的设计原则。这是工程师、计算机科学家和生物学家之间的一个跨学科项目，旨在通过研究鱼类和其他简单动物产生游泳运动的神经系统，更好地了解动物控制运动的方式。这项研究将包括研究特定动物的神经系统和对这些系统产生的行为进行建模。神经系统中产生观察到的运动模式的电路非常复杂，即使在这些相对简单的动物中也是如此。因此，这些研究人员的研究将集中在模拟生物功能的超大规模集成电路(VLSI)的设计上。这些复杂的人工电路使得实时创建神经元放电模式变得容易得多，这些模式与在生物同行中发现的非常相似。这项研究的最终目标是更好地理解生物学，同时学习如何更好地设计系统，执行目前只能由人类和其他动物完成的复杂任务。