It is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an \(L_{1}\) penalty. However, the partial correlations found in neural spike count data are neither large nor sparse, so techniques that perform well in sparse settings will typically perform poorly in the context of neural spike count data. Fortunately, the correlated firing for any pair of cortical neurons depends strongly on both their distance apart and the features for which they are tuned. We introduce a method that takes advantage of these known, strong effects by allowing the penalty to depend on them: thus, for example, the connection between pairs of neurons that are close together will be penalized less than pairs that are far apart. We show through simulations that this physiologically-motivated procedure performs substantially better than off-the-shelf generic tools, and we illustrate by applying the methodology to populations of neurons recorded with multielectrode arrays implanted in macaque visual cortex areas V1 and V4.

中文翻译：

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调整皮层协方差的正则化。

现在常见的是同时记录数十到数百或更多的神经元，并询问网络活动在实验条件下如何变化。解决功能连通性问题的自然框架是将高斯图形建模应用于神经数据，其中图形中的每个边对应于神经元之间的非零部分相关性。因为可能的边缘数量很大，所以一种估计图的策略是应用旨在使用\（L_ {1} \）识别大型稀疏效果的方法。罚款。但是，在神经峰值计数数据中发现的局部相关性既不大也不稀疏，因此在稀疏设置中表现良好的技术通常在神经峰值计数数据的情况下表现较差。幸运的是，任何一对皮层神经元的相关放电都强烈取决于它们之间的距离以及对其调整的功能。我们介绍一种通过允许惩罚依赖于它们的方法来利用这些已知的强效方法的方法：因此，例如，对彼此靠近的神经元对之间的连接的惩罚要小于相距较远的神经元对之间的惩罚。通过仿真显示，这种生理驱动的程序比现成的通用工具执行得更好，