Neural encoding of motor precision for advancing brain-machine interfaces

脑机接口（BMI）通过技术进步取得了迅速发展，这些技术进步允许长期记录大量神经元，更强大的计算能力，更好的训练和更好的回归算法来估计神经调节。然而，目前的BMI几乎完全依赖于固定的线性神经编码模型，这些模型对所有类型的运动都是相同的。这些固定的模型是相同的，无论运动是快还是慢，并且无论哪个自由度对于特定任务要求最高的精度都是相同的。在某种程度上，当前BMI的这种局限性反映了对大脑如何在不同精度水平上控制运动的基础知识不足。为了推进这一领域，本项目将测试以下假设：1）运动皮层的神经活动发生特定变化，这取决于所需的精度; 2）更灵活的非线性算法，可以调整精度并从多个线性解码器中进行主动选择，这将使BMI表现更接近正常人。 这一一般假设将通过两个具体目标进行检验。目的1将研究在伸手和抓握任务中，当指令精确度系统地变化时，运动精确度的选择性神经编码。目标2将研究使用新的神经信号输入到BMI输出变换来提高BMI性能。这项新颖的BMI实验将专注于使用神经活动的多个维度来提高沿着给定自由度的精度。 在追求这些具体目标，我将接受额外的培训，以推进我的职业生涯，并从我目前的博士后助理职位过渡到一个独立的，终身教职的教师，通过开发一个研究计划，桥接运动的神经控制，BMI设计和计算神经科学领域。在K99期间，我将在Sridevi Sarma和Robert Jacobs博士的指导下推进我在计算神经科学方面的培训。与Sarma博士一起，我将专注于使用点过程建模对神经活动进行统计建模。与Jacobs博士一起，我将专注于将混合模型和增益调度应用于BMI。到我的K99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向独立过渡的成功。

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.