Non-separable covariance models for spatio-temporal data, with applications to neural encoding analysis

Abstract

Neural encoding studies explore the relationships between measurements of neural activity and measurements of a behavior that is viewed as a response to that activity. The coupling between neural and behavioral measurements is typically imperfect and difficult to measure. To enhance our ability to understand neural encoding relationships, we propose that a behavioral measurement may be decomposable as a sum of two latent components, such that the direct neural influence and prediction is primarily localized to the component which encodes temporal dependence.For this purpose, we propose to use a non-separable Kronecker sum covariance model to characterize the behavioral data as the sum of terms with exclusively trial-wise, and exclusively temporal dependencies. We then utilize a corrected form of Lasso regression in combination with the nodewise regression approach for estimating the conditional independence relationships between and among variables for each component of the behavioral data, where normality is necessarily assumed. We provide the rate of convergence for estimating the precision matrices associated with the temporal as well as spatial components in the Kronecker sum model. We illustrate our methods and theory using simulated data, and data from a neural encoding study of hawkmoth flight; we demonstrate that the neural encoding signal for hawkmoth wing strokes is primarily localized to a latent component with temporal dependence, which is partially obscured by a second component with trial-wise dependencies.

SG-PALM: a Fast Physically Interpretable Tensor Graphical Model

“…Alternatively, Kalaitzis et al (2013); Greenewald et al (2019) proposed to model inverse covariance matrices using a KS representation. Rudelson & Zhou (2017); Park et al (2017) proposed KS-structured covariance model which corresponds to an errors-in-variables model. The KS (inverse) covariance structure corresponds to the Cartesian product of graphs (Kalaitzis et al, 2013;Greenewald et al, 2019), which leads to more parsimonious representations of (conditional) dependency than the KP.…”