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

项目名称：用于提高准确性和精度的自适应神经形态VLSI：蝙蝠回声定位的注意力建模摘要：回声是我们日常听觉生活的重要组成部分，但很少有人意识到我们经常使用它们来理解我们周围的空间。回声定位的蝙蝠以这些回声作为它们的主要感官（即声纳），在复杂的森林环境中表现出敏捷的飞行能力，并在飞行中成功捕获昆虫；一切都在黑暗中。因为大脑代表了一个计算引擎，它在能力和功耗方面远远超过任何一台人造计算机，所以我们对理解神经计算的原理很感兴趣。多年来，我们一直在构建定制的模拟集成电路，模仿蝙蝠回声定位的基本神经处理过程，努力在小型飞行器上展示这些原理，要求实时性能，小型化和功率效率。“神经形态”超大规模集成（VLSI）方法（通过模仿其结构形态来实现神经计算）在尺寸和功率方面具有优势，但通常缺乏所需的精度和灵活性。这一直是该研究领域长期可行性的主要问题之一。在本提案中，我们追求三个主要目标：1)扩大在电路层面的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；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），以指导这一学习过程。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音线索的认识，以及我们作为人类和我们制造的机器如何学会利用它们。

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.