Dynamical differential covariance recovers directional network structure in multiscale neural systems.

ABSTRACT: Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. W

SUBMITTER: Chen Y 

PROVIDER: S-EPMC9214501 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature