Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships.
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships.
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DOI：
10.1371/journal.pcbi.1009799
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发表时间：
2022-01
影响因子：
4.3
通讯作者：
Onken A
中科院分区：
文献类型：
--
作者：
Kudryashova N;Amvrosiadis T;Dupuy N;Rochefort N;Onken A
当前系统神经科学的主要目标之一是了解神经元群如何整合感官信息来告知行为。然而，估计在高维神经元群中编码的刺激或行为信息是具有挑战性的。我们提出了一种基于参数copula的方法，该方法可以对具有不同统计量和时间尺度特征的神经元和行为变量的联合分布进行建模。为了解释变量之间依赖关系的时间或空间变化，我们通过高斯过程（GP）对变化的copula参数进行建模。我们通过合成数据和清醒小鼠的神经元和行为记录验证了Copula-GP框架。我们表明，与其他非参数方法相比，在我们的方法中使用高维依赖结构的参数描述在高维互信息估计中提供了更好的准确性。此外，通过量化神经元和行为变量之间的冗余，我们的模型以一种无监督的方式暴露了奖励区域的位置（即，不使用任何关于任务结构的明确线索）。这些结果表明，Copula-GP框架对于分析神经元、感觉和行为变量之间复杂的多维关系特别有用。理解一组变量之间的关系是许多领域中常见的问题，例如天气预报或股票市场数据。在神经科学中，主要的挑战之一是表征神经元活动、感觉刺激和行为输出之间的依赖关系。建模这种统计依赖关系的一种选择方法是基于copula，它将依赖关系从单变量统计中分离出来。为了解释依赖关系的变化，我们通过高斯过程对copula参数的变化进行建模，该过程以任务相关变量为条件。我们方法的新颖之处包括：1)对依赖关系进行显式建模；2)结合不同的copula来描述实验观察到的变异性。我们通过合成数据和执行行为任务的小鼠视觉皮层的记录来验证拟合优度以及信息估计。与其他常用技术相比，我们的参数模型在描述高维依赖性方面表现出更好的性能。我们证明了我们的模型可以估计信息并预测任务的行为相关参数，而无需向模型提供任何明确的线索。我们的研究结果表明，我们的模型在神经科学应用的背景下是可解释的，可扩展到大数据集，适合于准确的统计建模和信息估计。
One of the main goals of current systems neuroscience is to understand how neuronal populations integrate sensory information to inform behavior. However, estimating stimulus or behavioral information that is encoded in high-dimensional neuronal populations is challenging. We propose a method based on parametric copulas which allows modeling joint distributions of neuronal and behavioral variables characterized by different statistics and timescales. To account for temporal or spatial changes in dependencies between variables, we model varying copula parameters by means of Gaussian Processes (GP). We validate the resulting Copula-GP framework on synthetic data and on neuronal and behavioral recordings obtained in awake mice. We show that the use of a parametric description of the high-dimensional dependence structure in our method provides better accuracy in mutual information estimation in higher dimensions compared to other non-parametric methods. Moreover, by quantifying the redundancy between neuronal and behavioral variables, our model exposed the location of the reward zone in an unsupervised manner (i.e., without using any explicit cues about the task structure). These results demonstrate that the Copula-GP framework is particularly useful for the analysis of complex multidimensional relationships between neuronal, sensory and behavioral variables. Understanding the relationship between a set of variables is a common problem in many fields, such as weather forecast or stock market data. In neuroscience, one of the main challenges is to characterize the dependencies between neuronal activity, sensory stimuli and behavioral outputs. A method of choice for modeling such statistical dependencies is based on copulas, which disentangle dependencies from single variable statistics. To account for changes in dependencies, we model changes in copula parameters by means of Gaussian Processes, conditioned on a task-related variable. The novelty of our approach includes 1) explicit modeling of the dependencies; and 2) combining different copulas to describe experimentally observed variability. We validate the goodness-of-fit as well as information estimates on synthetic data and on recordings from the visual cortex of mice performing a behavioral task. Our parametric model demonstrates significantly better performance in describing high dimensional dependencies compared to other commonly used techniques. We demonstrate that our model can estimate information and predict behaviorally-relevant parameters of the task without providing any explicit cues to the model. Our results indicate that our model is interpretable in the context of neuroscience applications, scalable to large datasets and suitable for accurate statistical modeling and information estimation.
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