Lee WeiHsien, Scherschligt Xavier, Nishimoto Matthew, Rouse Adam G
Neurosurgery Department, University of Kansas Medical Center, Kansas City, Kansas, USA.
Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA.
bioRxiv. 2025 Jul 3:2025.07.01.662682. doi: 10.1101/2025.07.01.662682.
Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.
运动皮层中的神经元群体发出自愿运动的信号。大多数经典神经编码模型和当前的脑机接口解码器都假定单个神经元沿着神经维度相加，以表示速度或力等运动特征。然而，大规模群体神经分析不断识别出随时间演变的多维神经活动轨迹。对这些神经轨迹的解释通常集中在皮质回路如何学习、组织和执行运动。然而，关于这些神经轨迹如何提高性能，特别是运动精度的描述却很缺乏。在本研究中，我们提出并测试了一个计算模型，该模型通过神经群体之间放电率的选择性共同激活和选择性定时，突出了神经轨迹在提高运动精度方面的作用。我们的模型以中心外伸展任务的实验结果为灵感，创建了几个用于运动神经编码的生理现实模型。使用递归神经网络来模拟下游神经元群体（如脊髓和运动单位）可能如何接收此类信息，我们表明，当跨时间整合特定于运动相位和/或幅度的神经信息，而不是采用仅基于瞬时速度的调谐模型时，运动更准确。我们的发现表明，精确的运动控制源于神经群体的时空募集，从而产生不同的神经轨迹。我们预计我们的结果不仅将显著影响运动皮层中运动神经编码的描述方式，还将影响未来对脑网络如何为运动规划和执行传递信息的理解。我们的模型还为如何在神经群体中纳入选择性激活以改进未来的脑机接口提供了潜在的灵感。
