Neuroscience: simulated nonlinear interacting
systems and physiological responses
Causality estimation. From Abstract: " Autoregressive modeling
is replaced by ...NPMR. NPMR quantifies interactions between a response variable (effect)
and a set of predictor variables (cause); here, we modified NPMR for model prediction. We
also demonstrate how ... the sensitivity Q, could be used to reveal the structure of the
underlying causal relationships. We apply CNPMR on artificial data with known ground truth
(5 datasets), as well as physiological data (2 datasets). CNPMR correctly identifies both
linear and nonlinear causal connections that are present in the artificial data, as well
as physiologically relevant connectivity in the real data, and does not seem to be
affected by filtering. The Sensitivity measure also provides useful information about the
latent connectivity.The proposed estimator addresses many of the limitations of linear
Granger causality and other nonlinear causality estimators. CNPMR is compared with
pairwise and conditional Granger causality (linear) and Kernel-Granger causality
(nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations
without any modifications to the main method. Its nonparametric nature, its ability to
capture nonlinear relationships and its robustness to filtering make it appealing for a
number of applications.