Neural encoding of motor precision for advancing brain-machine interfaces

用于推进脑机接口的运动精度的神经编码

• 批准号: 10058855
• 负责人: Adam G Rouse
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF KANSAS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058855
• 关键词: Accounting; Algorithms; Amyotrophic Lateral Sclerosis; Athletic; Behavior; Career Mobility; Chronic; Data; Devices; Dimensions; Discipline; Disease; Exhibits; Faculty; Freedom; Hour; Human; Intuition; Knowledge; Linear Models; Mentors; Methods; Modeling; Monkeys; Motor; Motor Activity; Motor Cortex; Motor Skills; Movement; Nervous System control; Neurons; Normal Range; Operative Surgical Procedures; Output; Paralysed; Patients; Performance; Phase; Population; Positioning Attribute; Postdoctoral Fellow; Posture; Process; Research; Schedule; Speed; Spinal cord injury; Statistical Models; Technology; Testing; Time; Training; Translating; Weight; arm; arm movement; artificial neural network; base; brain machine interface; bridge program; computational neuroscience; design; experimental study; flexibility; grasp; high dimensionality; improved; innovation; kinematics; member; mind control; neural model; neurophysiology; neuroprosthesis; neuroregulation; neurotransmission; novel; regression algorithm; relating to nervous system; sensory input; success; tenure track

Brain-machine interfaces (BMIs) have progressed rapidly through technological advances that allow recording chronically from large numbers of neurons, more computational power, better training, and better regression algorithms for estimating neural tuning. However, current BMIs rely almost exclusively on fixed, linear models of neural encoding that are the same for all types of movements. These fixed models are the same whether the movements are fast or slow, and the same no matter which degrees of freedom require the most precision for a particular task. In part, this limitation of current BMIs reflects inadequate basic knowledge concerning how the brain controls movement at different levels of precision. To advance the field, the present project will test the hypotheses that 1) specific changes occur in the neural activity of the motor cortex depending on the precision required, and 2) more flexible, non-linear algorithms that adjust precision and actively select from multiple linear decoders will enable BMI performance closer to that of normal humans. This general hypothesis will be tested with two Specific Aims. Aim 1 will examine selective neural encoding of movement precision when the instructed precision is varied systematically in a reach and grasp task. Aim 2 will examine improving BMI performance using novel neural signal input to BMI output transforms. This novel BMI experiment will focus on using multiple dimensions of neural activity to improve precision along a given degree of freedom. In pursuing these Specific Aims, I will receive additional training to advance my career and transition from my current post-doctoral associate position to that of an independent, tenure-track faculty member by developing a research program that bridges the fields of neural control of movement, BMI design, and computational neuroscience. During my K99 years, I will advance my training in computational neuroscience, mentored by Drs. Sridevi Sarma and Robert Jacobs. With Dr. Sarma, I will focus on statistical modeling of neural activity using point process modeling. With Dr. Jacobs, I will focus on applying mixture models and gain-scheduling for BMIs. By the end of my K99 years, I thus will have expertise in three complementary disciplines—motor neurophysiology, BMI, and computational neuroscience—all contributing to the success of my transition to independence.

脑机接口 (BMI) 凭借技术进步而迅速发展，这些技术进步允许长期记录大量神经元、更强的计算能力、更好的训练以及用于估计神经调节的更好的回归算法。然而，当前的 BMI 几乎完全依赖于固定的线性神经编码模型，这些模型对于所有类型的运动都是相同的。无论运动是快还是慢，这些固定模型都是相同的，并且无论特定任务的哪个自由度需要最精确度，这些固定模型都是相同的。在某种程度上，当前体重指数的这种局限性反映了关于大脑如何以不同精度水平控制运动的基础知识不足。为了推进该领域的发展，本项目将测试以下假设：1）运动皮层的神经活动根据所需的精度发生特定变化，2）更灵活的非线性算法可以调整精度并主动从多个线性解码器中进行选择，从而使 BMI 性能更接近正常人。 这一一般假设将通过两个具体目标进行检验。目标 1 将检查当指令精度在触及和抓取任务中系统变化时运动精度的选择性神经编码。目标 2 将研究使用新颖的神经信号输入到 BMI 输出变换来提高 BMI 性能。这项新颖的 BMI 实验将侧重于利用神经活动的多个维度来提高给定自由度的精度。 在追求这些具体目标的过程中，我将接受额外的培训，通过开发一个连接运动神经控制、BMI设计和计算神经科学领域的研究项目，以推进我的职业生涯，并从目前的博士后助理职位过渡到独立的终身教授职位。在我的 K99 岁月里，我将在 Drs. 的指导下推进计算神经科学方面的培训。斯里黛薇·萨尔玛和罗伯特·雅各布斯。我将与 Sarma 博士一起专注于使用点过程建模对神经活动进行统计建模。我将与 Jacobs 博士一起专注于应用混合模型和 BMI 的增益调度。因此，到 K99 学年结束时，我将拥有三个互补学科的专业知识——运动神经生理学、BMI 和计算神经科学——所有这些都有助于我成功过渡到独立。