Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

ABSTRACT: Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, c

SUBMITTER: Sheikhattar A 

PROVIDER: S-EPMC5924897 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature