  With modern calcium imaging technology, activities of thousands of neurons
can be recorded in vivo. These experiments can potentially provide new insights
into intrinsic functional neuronal connectivity, defined as contemporaneous
correlations between neuronal activities. As a common tool for estimating
conditional dependencies in high-dimensional settings, graphical models are a
natural choice for estimating functional connectivity networks. However, raw
neuronal activity data presents a unique challenge: the relevant information in
the data lies in rare extreme value observations that indicate neuronal firing,
rather than in the observations near the mean. Existing graphical modeling
techniques for extreme values rely on binning or thresholding observations,
which may not be appropriate for calcium imaging data. In this paper, we
develop a novel class of graphical models, called the Subbotin graphical model,
which finds sparse conditional dependency structures with respect to the
extreme value observations without requiring data pre-processing. We first
derive the form of the Subbotin graphical model and show the conditions under
which it is normalizable. We then study the empirical performance of the
Subbotin graphical model and compare it to existing extreme value graphical
modeling techniques and functional connectivity models from neuroscience
through several simulation studies as well as a real-world calcium imaging data
example.
