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

项目摘要。应聘者和职业目标：我是一名训练有素的工程师，有很强的 神经工程和运动脑机接口(BMI)的发展。我的职业目标是建立 一个独立的非人灵长类(NHP)实验室，有两个主要目标。首先，我将推进我们的根本 通过将电生理学与新的统计学和统计学相结合来理解运动系统 计算方法。其次，我将利用这些知识为优秀的BMI系统开发框架。 在我的学术和研究生涯中，我积累了工程、计算、 和神经科学，目标是追求这些目标。机器学习、大规模神经网络的研究进展 神经网络中的录音和深度学习正在迅速发生(部分通过大脑倡议)，以及 在这一领域非常有前途。然而，很少有研究人员拥有可以利用的正确的技能组合 他们在我感兴趣的领域。在完成拟议的培训后，我将处于独特的地位，能够执行 促进我们对皮质控制的规划和实施的理解所需的创新工作 动静。我将培训下一代科学家和工程师在实验和计算方面 理解大脑皮层计算基本原理所需的方法。 研究计划：在这个项目中，我将使用多种计算方法来理解 跨多种行为的运动皮质(M1)群体活动的结构。然后我会用那个 具备创建适用于各种运动的高性能BMI解码器的知识。 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 在完成任务(例如，到达)时，神经活动似乎存在于它完全探索的小空间内。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在许多 更大的空间。这意味着不同动作的活动模式不会彼此“接近”或重叠。 虽然违反直觉，但这种几何学带来了新的机会。通过利用活动模式的分离， 即使在同时展开时，动作也很容易辨别出来。我将进一步探索这个几何学 通过灵长类动物和神经网络模型中的多种行为，开发新的BMI方法。这个 该计划的具体目标是(1)为新型轮椅相关导航创造一个高性能的解码器 任务，(2)构建网络模型以了解M1活动结构并确定将 跨任务(到达、导航)进行泛化，以及(3)使用统一解码器实现多任务BMI 允许动物导航并与对象交互。 职业发展计划：我将在哥伦比亚大学接受Mark Churchland博士和Larry Abbott博士的培训。

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.