Implementation and Analysis of Neuromorphic Motor-Pattern Generating Systems

神经形态运动模式生成系统的实现和分析

• 批准号: 0131612
• 负责人: Stephen DeWeerth
• 金额: $ 45万
• 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: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0131612&HistoricalAwards=false
• 关键词: Implementation; Analysis; Neuromorphic; Motor; Pattern

The generation of rhythmic movement is essential to locomotion and many other activities in most animals. The pattern-generating networks in the brain that control these movements are efficient and reliable, and so provide a platform to study a critical set of biological control paradigms, and offer the potential to inspire engineered systems that exploit these underlying principles. Neuromorphic engineering is the development of hardware-based systems that are inspired by circuit architectures found in biological nervous systems. The goals of this collaborative project are to develop neuromorphic systems that emulate the control and production of such rhythmic movements, and to use nonlinear dynamical analysis to gain a better understanding of the production of these movements. Building on prior development of very large-scale integrated (VLSI) circuits that mimic motor patterns, and channel-based model neurons using such circuits, this project will create neuromorphic oscillators based on the dynamics of ionic channels in biological neurons, combine these oscillators with mechanical actuation and sensory feedback, and expand these systems to include heterogeneous populations of neurons. The models will test the engineering validity of some principles inferred from biological designs, and will in turn allow quantitative testing of how varied parameters, sensory feedback, and heterogeneous model populations may have effects in actual complex biological systems. Results from this work will extend beyond neuroengineering, to a better understanding of motor control and feedback in neurobiology, and to potential applications in robotic design. This collaborative project also continues training of students from various backgrounds and at various levels in laboratories with a solid history of interdisciplinary training.

有节奏运动的产生对于大多数动物的运动和许多其他活动是必不可少的。 大脑中控制这些运动的模式生成网络是有效和可靠的，因此提供了一个平台来研究一组关键的生物控制范式，并提供了激发利用这些基本原理的工程系统的潜力。 神经形态工程是基于硬件的系统的开发，其灵感来自于生物神经系统中发现的电路架构。 这个合作项目的目标是开发神经形态系统，模仿这种节奏运动的控制和生产，并使用非线性动力学分析，以获得更好地了解这些运动的生产。 基于先前开发的模仿运动模式的超大规模集成（VLSI）电路和使用此类电路的基于通道的模型神经元，该项目将基于生物神经元中离子通道的动力学创建神经形态振荡器，将这些振荡器与机械驱动和感觉反馈联合收割机相结合，并将这些系统扩展到包括异质神经元群体。 这些模型将测试从生物设计中推断出的一些原理的工程有效性，并反过来允许定量测试不同的参数，感觉反馈和异质模型群体在实际复杂的生物系统中可能产生的影响。 这项工作的结果将超越神经工程，更好地理解神经生物学中的运动控制和反馈，并在机器人设计中有潜在的应用。 这个合作项目还继续在实验室培训来自不同背景和不同级别的学生，这些学生具有跨学科培训的坚实历史。