Effective Reduced Diffusion-Models: A Data Driven Approach to the Analysis of Neuronal Dynamics
Effective Reduced Diffusion-Models: A Data Driven Approach to the Analysis of Neuronal Dynamics
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DOI：
10.1371/journal.pcbi.1000587
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发表时间：
2009-12-01
影响因子：
4.3
通讯作者：
Sanchez Vives, Maria V.
中科院分区：
文献类型：
--
作者：
Deco, Gustavo;Marti, Daniel;Sanchez Vives, Maria V.
本文介绍了一种将神经动力学数据简化为有效扩散方程的新方法，无论是实验还是使用生物物理详细模型的模拟。数据的维数首先被简化为第一主成分，然后用一维平均场朗之万方程的平稳解进行拟合，朗之万方程描述了布朗粒子在势中的运动。这种描述的优点是可以很容易地推导出动态变量的平稳概率密度。我们将这种方法应用于麻醉动物在上下状态下的皮质网络动力学分析。在深度麻醉期间，细胞内记录的上下状态转换发生了高度规律性，无法用一维扩散方程充分描述。然而，在较轻的麻醉下，这种模型更适合于在上下状态下花费的时间分布，这表明噪音在决定在特定状态下花费的时间方面发挥了作用。
We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.