Analysis of Distributed Neural Synchrony through State-Space Coherence Analysis

Abstract

Established methods to track the dynamics of neural representations focus at the level of individual neurons for spiking data, and individual or pair of channels for local field potentials. However, our understanding of neural function and computation has moved toward an integrative view, based upon coordinated activity of multiple neural populations across brain areas. To draw network-level inferences of brain function, we propose a new modeling framework that combines the state-space model and cross-spectral matrix estimates – this is called state-space coherence (SSCoh). We define elements of the SSCoh and derive system identification and approximate filter solution for multivariate space processes. We expand SCoh for mixed observation processes, where the observation includes different modalities of neural data including local filed potential and spiking activity. Finally, we show an application of the framework to study neural synchrony across different brain nodes of a task participant performing Stroop task under different distraction levels.

Synchrony-Division Neural Multiplexing: An Encoding Model

“…We also can use more flexible parametric function (with parameter set θ) like ex-quadratic function 𝑓 𝜃 (𝑥) as the static nonlinearities. By using ex-quadratic function as nonlinearities we eventually need to optimize a convex cost function, Which gives the optimum parameters set θ for the nonlinearity and can be optimally optimized by a maximumlikelihood (ML) algorithm (details in the Appendix B) [9,27,28].…”