390 citations · 485 across the 6 of their papers we have counts for
6 papers
Granger Causality: Basic Theory and Application to Neuroscience
Mingzhou Ding, Yonghong Chen, Steven L. Bressler
Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neura…
Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data
Yonghong Chen, Steven L. Bressler, Mingzhou Ding
It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this…
Analyzing Stability of Equilibrium Points in Neural Networks: A General Approach
Wilson A. Truccolo, Govindan Rangarajan, Yonghong Chen +1
Networks of coupled neural systems represent an important class of models in computational neuroscience. In some applications it is required that equilibrium points in these networ…
Analyzing Multiple Nonlinear Time Series with Extended Granger Causality
Yonghong Chen, Govindan Rangarajan, Jianfeng Feng +1
Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a si…
Generalized Turing Patterns and Their Selective Realization in Spatiotemporal Systems
Govindan Rangarajan, Yonghong Chen, Mingzhou Ding
We consider the pattern formation problem in coupled identical systems after the global synchronized state becomes unstable. Based on analytical results relating the coupling stren…
General Stability Analysis of Synchronized Dynamics in Coupled Systems
Yonghong Chen, Govindan Rangarajan, Mingzhou Ding
We consider the stability of synchronized states (including equilibrium point, periodic orbit or chaotic attractor) in arbitrarily coupled dynamical systems (maps or ordinary diffe…