Bahari Fatemeh, Tulyaganova Camila, Billard Myles, Alloway Kevin, Gluckman Bruce J
Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802.
Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802.
Conf Rec Asilomar Conf Signals Syst Comput. 2016 Nov;2016:1061-1065. doi: 10.1109/ACSSC.2016.7869532. Epub 2017 Mar 6.
Sleep is important for normal brain function, and sleep disruption is comorbid with many neurological diseases. There is a growing mechanistic understanding of the neurological basis for sleep regulation that is beginning to lead to mechanistic mathematically described models. It is our objective to validate the predictive capacity of such models using data assimilation (DA) methods. If such methods are successful, and the models accurately describe enough of the mechanistic functions of the physical system, then they can be used as sophisticated observation systems to reveal both system changes and sources of dysfunction with neurological diseases and identify routes to intervene. Here we report on extensions to our initial efforts [1] at applying unscented Kalman Filter (UKF) to models of sleep regulation on three fronts: tools for multi-parameter fitting; a sophisticated observation model to apply the UKF for observations of behavioral state; and comparison with data recorded from brainstem cell groups thought to regulate sleep.
睡眠对于正常的大脑功能很重要,而睡眠中断与许多神经疾病共存。人们对睡眠调节的神经学基础的机制理解不断加深,这开始催生以数学方式描述机制的模型。我们的目标是使用数据同化(DA)方法验证此类模型的预测能力。如果这些方法成功,并且模型能够准确描述物理系统的足够多的机制功能,那么它们就可以用作精密的观测系统,以揭示神经疾病中的系统变化和功能障碍来源,并确定干预途径。在此,我们报告在将无迹卡尔曼滤波器(UKF)应用于睡眠调节模型的初步工作[1]基础上,在三个方面的扩展:多参数拟合工具;用于将UKF应用于行为状态观测的精密观测模型;以及与被认为调节睡眠的脑干细胞群记录的数据进行比较。