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.

通过技术进步，脑机界面（BMI）迅速发展，这些进步使大量神经元，更多的计算能力，更好的训练和更好的回归算法以估算神经调节。但是，当前的BMI几乎完全依赖于固定的，神经编码的线性线性模型，这些模型对于所有类型的运动都是相同的。这些固定模型是相同的，无论运动是快速还是缓慢，无论哪个自由度都需要最精确的特定任务。当前BMI的这种局限性在某种程度上反映了有关大脑如何以不同精度控制运动的基本知识不足。为了推进该领域，本项目将检验以下假设，即1）运动皮层的神经元活性发生的特定变化，具体取决于所需的精度，2）更灵活的非线性算法，这些算法会调整精度并从多个线性解码器中进行积极选择，并从多个线性解码器中进行积极选择，将使BMI表现更接近正常人的BMI表现。该总体假设将以两个具体的目的进行检验。 AIM 1将检查运动精度的选择性神经编码时，当指示精度在触及和掌握任务中系统地变化时。 AIM 2将使用新型神经信号输入到BMI输出变换来检查BMI性能的提高。这个新型的BMI实验将集中于使用神经活性的多个维度，以提高给定自由度的精度。在追求这些特定目标时，我将通过制定一项研究计划，从而从当前的博士后助理职位转变为一个独立的，终身轨道教师的职业，并通过制定一项研究计划，该计划弥合了运动，BMI设计，BMI设计和计算神经科学的领域。在我的K99年中，我将进步由Drs进行的计算神经科学培训。 Sridevi Sarma和Robert Jacobs。对于SARMA博士，我将使用点过程建模专注于神经活性的统计建模。对于Jacobs博士，我将重点放在应用BMI的混合物模型和增益式划分上。因此，到我的K99年结束时，我将在三个完整的学科（Motor Neurophrosyology，BMI和计算神经科学）方面拥有专业知识，这都有助于我过渡到独立性的成功。