CRCNS: Transcortical and spinal circuit contributions to hand shaping in primates - Real-time neuromorphic implementation for robotic demonstration

CRCNS：跨皮层和脊髓回路对灵长类动物手部塑形的贡献 - 机器人演示的实时神经形态实现

• 批准号: 2113096
• 负责人: Francisco Valero-Cuevas
• 金额: $ 94.68万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113096&HistoricalAwards=false
• 关键词: CRCNS; Transcortical; spinal; circuit; contributions

Engineering of robots primarily relies on prescribed algorithms for centralized control. This results in robots with limited versatility because every function must be preprogrammed. Animals, by contrast, rely on adaptable neuronal networks distributed throughout the body that convert and modulate brain signals into specific and well-coordinated muscle actions and corrections. It has now become possible to record signals from these large neuronal networks in primates in the part of the spinal cord controlling hand function. Therefore, our goal is to extract the functional features of these neuronal networks, and validate their function by controlling bio-inspired robotic hands, as well as human cadaveric hands. This validation will allow the first physical test of the biological mechanisms for grasp function, and will help understand hand disabilities and treatments in, for example, stroke, spinal cord injury and cerebral palsy. It will also launch a new generation of versatile robots that use the mechanisms of our nervous system.The overall goal is to create a synthetic functional analogue of the cervical spine that controls multiple grasp modalities in bio-robotic hands. This is made possible by the advent of specialized massively parallel computer chips that allow the implementation of networks of hundreds of simulated neurons and their spiking dynamics (neuromorphic chips). Therefore, in this project, we will extract network architectures for the control of the hand from the nervous system of primates (Japan) and implement them as neuromorphic circuits to create a new class of versatile robotic hands (USA). Using specialized recording system, will record neural data from hundreds of spinal interneurons and alpha motoneurons in the cervical spinal cord of awake, behaving monkeys during manipulation—while also recording EMG and hand kinematics. This will be the most complete data set to date for cervical control of the hand (Aim 1). Then, we will create neuromorphic implementations of that neural circuitry using state of the art very large scale integration chips. Special attention will be paid to implementing physiologically valid versions of alpha-gamma motoneuron interactions, and realistic plasticity rules. We will also create a Domain Specific Language that allows the translation of general neuroanatomical circuits into neuromorphic code to make this technology accessible by the general neuroscience community (Aim 2). We will test, refine and validate the neuromorphic circuits by using the neuromorphic chips to control neuro-robotic hands using electric motors programmed to behave as muscles, and sensors to replicate the function of muscle spindles and Golgi tendon organs (Aim 3). We will also control cadaveric human hands to validate the neuromorphic controller for the anatomy of the human hand. This will pave the way to a better understanding of hand function and disability and serve as the proof of concept for a new class of neuromechanical robotic, prosthetic and brain-controlled hands.A companion project is being funded by the National Institute of Information and Communications Technology, Japan (NICT). This project is jointly funded by the following NSF programs: Disability and Rehabilitation Engineering, Collaborative Research in Computational Neuroscience, Robust Intelligence, and Mind, Machine and Motor Nexus program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器人的工程主要依靠规定的算法进行集中控制。这导致机器人的通用性有限，因为每个功能都必须预先编程。相比之下，动物依赖于分布在全身的适应性神经元网络，这些网络将大脑信号转换和调制为特定的、协调良好的肌肉动作和矫正。现在已经有可能在灵长类动物脊髓中控制手功能的部分记录来自这些大型神经元网络的信号。因此，我们的目标是提取这些神经元网络的功能特征，并通过控制仿生机械手和人类身体手来验证它们的功能。这一验证将允许对抓握功能的生物机制进行第一次物理测试，并将有助于了解手残疾和中风、脊髓损伤和脑瘫等治疗方法。它还将推出使用我们神经系统机制的新一代多功能机器人。总体目标是创建一种合成的功能模拟颈椎，在生物机器人手中控制多种抓取方式。这是由于专门的大规模并行计算机芯片的出现，这种芯片允许实现由数百个模拟神经元及其尖峰动力学(神经形态芯片)组成的网络。因此，在这个项目中，我们将从灵长类动物的神经系统中提取用于控制手的网络架构(日本)，并将它们作为神经形态电路来实现，以创建一种新的多功能机器人手(美国)。使用专门的记录系统，将记录来自清醒的颈髓中数百个脊髓中间神经元和阿尔法运动神经元的神经数据，在操作过程中表现为猴子-同时还记录肌电和手部运动学。这将是迄今为止手部颈部控制的最完整的数据集(目标1)。然后，我们将使用最先进的超大规模集成芯片来创建神经电路的神经形态实现。将特别注意实施阿尔法-伽马运动神经元相互作用的生理有效版本，以及现实的可塑性规则。我们还将创建一种领域特定语言，允许将一般神经解剖电路转换为神经形态代码，以使普通神经科学界能够访问这项技术(目标2)。我们将通过使用神经形态芯片来测试、改进和验证神经形态电路，这些芯片使用编程为表现为肌肉的电动马达来控制神经机器人手，以及复制肌梭和高尔基肌腱器官的功能的传感器(目标3)。我们还将控制身体的手，以验证神经形态控制器对人类手的解剖。这将为更好地了解手的功能和残疾铺平道路，并为新型神经机械机器人、假肢和脑控手提供概念验证。日本国家信息与通信技术研究所(NICT)正在资助一个配套项目。该项目由以下NSF项目共同资助：残疾和康复工程、计算神经科学中的合作研究、稳健智能以及思维、机器和运动关联计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。