CAREER: Adaptive Neuromorphic VLSI for Improving Accuracy and Precision: Modeling Attention for Bat Echolocation

职业：用于提高准确性和精确度的自适应神经形态 VLSI：蝙蝠回声定位注意力建模

• 批准号: 0347573
• 负责人: Timothy Horiuchi
• 金额: $ 40万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347573&HistoricalAwards=false
• 关键词: CAREER; Adaptive; Neuromorphic; VLSI; Improving

PROPOSAL NO: 0347573INSTITUTION: University of Maryland College ParkPRINCIPAL INVESTIGATOR: Horiuchi ,TimothyTITLE: Adaptive Neuromorphic VLSI for Improving Accuracy and Precision:Modeling Attention for Bat EcholocationAbstractEchoes are a major part of our daily auditory lives, yet very few of us are even aware that we use them constantly to understand the space around us. Echolocating bats have made these echoes their primary sense (i.e. sonar), demonstrating agile flight in complex forest environments and successful insect capture in flight; all in darkness. Because the brain represents a computational engine that far surpasses any single man-made computer in both capabilities and power consumption, we are interested to understand the principles of neural computation.For a number of years we have been constructing custom analog integrated circuits that mimic the basic neural processing of bat echolocation in an effort to demonstrate these principles on a small flying vehicle, requiring real-time performance, miniaturization, and power efficiency.The 'neuromorphic' very-large-scale-integration (VLSI) approach (which implements neural computations by mimicking its structural morphology), offers advantages in size and power, but often lacks needed precision and flexibility. This has been one of the main concerns about this research field's long-term viability. In this proposal we pursue three main goals: 1) to expand the use of learning and adaptation techniques at the circuit level to increase computational precision and accuracy at the system-level, 2) to explore the use of 'virtual' wiring to mimic large-scale wiring changes akin to neural development, and 3) to adapt existing neural models of spatial attention for use in sampled sensory systems (e.g., sonar) to guide this learning process. Through this research and our educational plan we hope to raise awareness to the rich acoustic cues around us and how we as humans and the machines we build can learn to make use of them.

提案编号：0347573研究机构：马里兰州大学公园校长：堀内，小野标题：自适应神经形态超大规模集成电路提高准确性和精度：建模注意力蝙蝠回声摘要回声是我们日常听觉生活的一个重要组成部分，但很少有人意识到，我们不断地使用它们来了解我们周围的空间。 回声定位蝙蝠已经将这些回声作为它们的主要感觉（即声纳），在复杂的森林环境中展示了敏捷的飞行和成功的飞行昆虫捕捉;所有这些都是在黑暗中进行的。 因为大脑代表了一个计算引擎，在能力和功耗方面远远超过任何单一的人造计算机，我们有兴趣了解神经计算的原理。多年来，我们一直在构建模拟蝙蝠回声定位的基本神经处理的定制模拟集成电路，试图在小型飞行器上展示这些原理，需要实时性能，“神经形态”超大规模集成（VLSI）方法（其通过模仿其结构形态来实现神经计算）提供了尺寸和功率方面的优势，但通常缺乏所需的精度和灵活性。 这一直是对这一研究领域的长期可行性的主要关切之一。 在这项提案中，我们追求三个主要目标：1）在电路层面扩大学习和适应技术的使用，以提高系统层面的计算精度和准确性，2）探索使用“虚拟”布线来模拟类似于神经发育的大规模布线变化，以及3）适应现有的空间注意力神经模型，用于采样感觉系统（例如，声纳）来指导这个学习过程。 通过这项研究和我们的教育计划，我们希望提高人们对我们周围丰富的声学线索的认识，以及我们作为人类和我们建造的机器如何学习利用它们。