睡眠调节的神经基础--从动物到模型的数据同化。

Fatemeh Bahari, Camila Tulyaganova, Myles Billard, Kevin Alloway, Bruce J Gluckman
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引用次数: 0

摘要

睡眠对大脑的正常功能非常重要,睡眠障碍与许多神经系统疾病并发。人们对睡眠调节的神经基础有了越来越多的机理认识,并开始建立机理数学模型。我们的目标是利用数据同化(DA)方法验证这些模型的预测能力。如果这些方法取得成功,并且模型能够准确描述物理系统的足够机理功能,那么它们就可以用作复杂的观测系统,揭示神经系统疾病的系统变化和功能障碍来源,并确定干预路径。在此,我们从三个方面报告了我们最初[1]将无香味卡尔曼滤波(UKF)应用于睡眠调节模型的工作的扩展:多参数拟合工具;应用UKF观察行为状态的复杂观察模型;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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Neural Basis for Sleep Regulation - Data Assimilation from Animal to Model.

The Neural Basis for Sleep Regulation - Data Assimilation from Animal to Model.

The Neural Basis for Sleep Regulation - Data Assimilation from Animal to Model.

The Neural Basis for Sleep Regulation - Data Assimilation from Animal to Model.

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.

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