Characterizing the structure of motor cortex activity across multiple behaviors for improved brain-machine interfaces

表征多种行为中运动皮层活动的结构，以改善脑机接口

• 批准号: 10580965
• 负责人: Karen Elizabeth Schroeder
• 金额: $ 7.79万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10580965
• 关键词: Animals; Area; BRAIN initiative; Behavior; Brain; Clinical Trials; Computers; Computing Methodologies; Development; Development Plans; Devices; Electrophysiology (science); Engineering; Geometry; Goals; Human; Individual; Intuition; Knowledge; Laboratories; Limb Prosthesis; Locomotion; Machine Learning; Methods; Motor; Motor Cortex; Movement; Neural Network Simulation; Paralysed; Pattern; Performance; Population; Positioning Attribute; Primates; Research; Research Personnel; Scientist; Statistical Methods; Structure; System; Training; Universities; Wheelchairs; Work; brain machine interface; career; career development; computational neuroscience; deep learning; grasp; improved; innovation; interest; limb loss; multitask; network models; neural network; neurotechnology; next generation; nonhuman primate; novel; relating to nervous system; skills

Project Abstract. Candidate and career goals: I am an engineer by training, with a strong background in neural engineering and the development of motor brain-machine interfaces (BMIs). My career goal is to establish an independent nonhuman primate (NHP) laboratory with two primary aims. First, I will advance our fundamental understanding of the motor system via the combination of electrophysiology with novel statistical and computational methods. Second, I will leverage this knowledge to develop frameworks for superior BMI systems. Throughout my academic and research career I have developed expertise in engineering, computation, and neuroscience with the goal of pursuing these aims. Advances in machine learning, large-scale neural recordings, and deep learning in neural networks are happening quickly (in part via the BRAIN Initiative), and are very promising for the field. Yet very few researchers have the correct combination of skills to make use of them in my areas of interest. In completing the proposed training, I will be uniquely positioned to perform the innovative work necessary to advance our understanding of the planning and execution of cortically controlled movements. I will train the next generation of scientists and engineers in the experimental and computational methods necessary to understand fundamental principles of cortical computations. Research plan: In this project, I will employ multiple computational approaches to understand the structure of population activity in motor cortex (M1) across multiple kinds of behaviors. I will then use that knowledge to create high performance BMI decoders that will be applicable to a wide range of movements. Recent empirical observations are changing our view of the structure of M1 activity. During one particular task (e.g., reaching), neural activity may seem to exist within a small space that it explores completely. Yet as more tasks are observed, it becomes clear that activity comprises a highly structured geometry within a much larger space. This means that activity patters for different movements do not come ‘near’ one another or overlap. While counterintuitive, this geometry yields new opportunities. By exploiting the separation of activity patterns, movements can be readily distinguished, even when unfolding simultaneously. I will further explore this geometry across multiple behaviors, both in primates and neural network models, to develop new BMI methods. The specific aims of the plan are to (1) create a high-performance decoder for a novel wheelchair-relevant navigation task, (2) build network models to understand M1 activity structure and identify decoding principles that will generalize across tasks (reaching, navigation), and (3) implement a multitask BMI using a unified decoder that allows animals to both navigate and interact with objects. Career development plan: I will be trained by Dr. Mark Churchland and Dr. Larry Abbott at Columbia University.

项目摘要。候选人和职业目标：我是一名受过培训的工程师，有很强的工程背景。 神经工程和运动脑机接口（BMI）的发展。我的职业目标是 一个独立的非人类灵长类动物（NHP）实验室，有两个主要目标。首先，我将提出我们的基本原则， 通过电生理学与新的统计和 计算方法其次，我将利用这些知识来开发上级BMI系统的框架。 在我的学术和研究生涯中，我在工程，计算， 和神经科学来实现这些目标。机器学习、大规模神经网络 录音和神经网络中的深度学习正在迅速发生（部分通过BRAIN Initiative）， 在这个领域非常有前途。然而，很少有研究人员能够正确地利用这些技能， 在我感兴趣的领域。在完成拟议的培训，我将是唯一的定位，以执行 创新的工作，必要的，以促进我们的规划和执行皮质控制的理解， 动作我将培养下一代的科学家和工程师在实验和计算 理解皮层计算的基本原理所必需的方法。 研究计划：在这个项目中，我将采用多种计算方法来理解 运动皮层（M1）的群体活动结构跨越多种行为。我会用它 知识，以创建高性能BMI解码器，将适用于广泛的运动。 最近的经验观察正在改变我们对M1活动结构的看法。在一次特殊的 任务（例如，到达），神经活动可能似乎存在于一个小的空间，它完全探索。然而作为 观察到更多的任务，很明显，活动包括一个高度结构化的几何形状， 更大的空间。这意味着不同运动的活动模式不会彼此“接近”或重叠。 虽然违反直觉，但这种几何形状带来了新的机会。通过利用活动模式的分离， 即使在同时展开时，也可以容易地区分运动。我将进一步探讨这种几何学 在灵长类动物和神经网络模型中的多个行为之间，开发新的BMI方法。的 该计划的具体目标是（1）为新型轮椅相关导航创建高性能解码器 任务，（2）建立网络模型，以了解M1活动结构，并确定解码原则， 跨任务（到达、导航）进行概括，以及（3）使用统一的解码器来实现多任务BMI，该解码器 让动物既能导航又能与物体互动。 职业发展计划：我将在哥伦比亚大学接受马克·丘奇兰博士和拉里·艾伯特博士的培训。