Until date, several machine learning approaches have been proposed for the dynamic
modeling of temporal omics data. Although they have yielded impressive results in terms of …

The causal connectivity of a network is often inferred to understand network function. It is
arguably acknowledged that the inferred causal connectivity relies on the causality measure …

The Granger causality (GC) analysis has been extensively applied to infer causal
interactions in dynamical systems arising from economy and finance, physics …

Statistically inferred neuronal connections from observed spike train data are often skewed
from ground truth by factors such as model mismatch, unobserved neurons, and limited data …

The recent development of genetically encoded calcium indicators enables monitoring in
vivo the activity of neuronal populations. Most analysis of these calcium transients relies on …

At present, network model is a general framework for the representation of complex system,
and its structure is the fundamental and prerequisite for control and other applications of …

Neurostimulation is a promising therapy for abating epileptic seizures. However, it is
extremely difficult to identify optimal stimulation patterns experimentally. In this study, human …

Despite the traditional view of parietal cortex as an important region for perceptual decision-
making, recent evidence suggests that sensory accumulation occurs simultaneously across …

Determining the structure of a network is of central importance to understanding its function
in both neuroscience and applied mathematics. However, recovering the structural …

Granger causality (GC) is a powerful method for causal inference for time series. In general,
the GC value is computed using discrete time series sampled from continuous-time …