Implementation and Analysis of Neuromorphic Motor-Pattern Generating Systems

韵律运动的产生对大多数动物的运动和许多其他活动至关重要。大脑中控制这些运动的模式生成网络是有效和可靠的，因此为研究一套关键的生物控制范式提供了一个平台，并提供了激发利用这些潜在原理的工程系统的潜力。神经形态工程是基于硬件系统的发展，其灵感来自于生物神经系统中的电路结构。这个合作项目的目标是开发神经形态系统，模拟这种有节奏的运动的控制和产生，并使用非线性动力学分析来更好地理解这些运动的产生。在先前开发的模拟运动模式的大规模集成电路（VLSI）和使用此类电路的基于通道的模型神经元的基础上，该项目将创建基于生物神经元中离子通道动力学的神经形态振荡器，将这些振荡器与机械驱动和感觉反馈结合起来，并扩展这些系统以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，并将反过来允许对不同参数、感觉反馈和异质模型种群在实际复杂生物系统中可能产生的影响进行定量测试。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。这一合作项目还将继续在具有扎实跨学科培训历史的实验室中对来自不同背景和不同层次的学生进行培训。

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.