Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex
Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex
复制标题
猕猴前额叶皮层尖峰计数的短期噪声依赖性建模
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
K. Obermayer
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作者：
A. Onken;S. Grünewälder;M. Munk;K. Obermayer
尖峰计数之间的相关性经常被用来分析神经编码。通常假设噪声是高斯的。然而，这种假设往往是不适当的，特别是对于低尖峰计数。在这项研究中，我们提出Copula作为一种替代方法。使用Copula可以使用任意的边缘分布，例如泊松分布或负二项分布，它们更适合于对尖峰计数的噪声分布进行建模。此外，Copula提供了广泛的依赖结构，可用于分析高阶相互作用。我们开发了一个框架来分析尖峰计数数据通过copula。提供了基于最大似然估计的参数推断和计算互信息的方法。我们应用的方法，我们的数据记录从猕猴前额叶皮层。数据分析导致三个发现：（1）基于Copula的分布提供了显着更好的拟合比离散化的多元正态分布;（2）负二项利润率拟合数据显着优于泊松利润率;和（3）的依赖结构进行12%的刺激和反应之间的互信息。
Correlations between spike counts are often used to analyze neural coding. The noise is typically assumed to be Gaussian. Yet, this assumption is often inappropriate, especially for low spike counts. In this study, we present copulas as an alternative approach. With copulas it is possible to use arbitrary marginal distributions such as Poisson or negative binomial that are better suited for modeling noise distributions of spike counts. Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions. We develop a framework to analyze spike count data by means of copulas. Methods for parameter inference based on maximum likelihood estimates and for computation of mutual information are provided. We apply the method to our data recorded from macaque prefrontal cortex. The data analysis leads to three findings: (1) copula-based distributions provide significantly better fits than discretized multivariate normal distributions; (2) negative binomial margins fit the data significantly better than Poisson margins; and (3) the dependence structure carries 12% of the mutual information between stimuli and responses.