In this paper, a new method based on delayed pattern causality is proposed, called spectral
time-varying pattern causality. Specifically, this method uses symbolic dynamics and phase …

Objective: Studying directed connectivity within spiking neuron networks can help
understand neural mechanisms. Existing methods assume linear time-invariant neural …

Introduction Information transmission and representation in both natural and artificial
networks is dependent on connectivity between units. Biological neurons, in addition …

The primate brain uses billions of interacting neurons to produce macroscopic dynamics and
behavior, but current methods only allow neuroscientists to investigate a subset of the neural …

Uncovering information hidden within brain networks can be a daunting task, especially in
the cases of abnormal, disrupted neural networks, such as epilepsy. Here, I present a …

The brain is a complex system with intricate neural dynamics, exhibiting interactions that are
thought to be crucial for emergent cognitive functions. Causality methods provide a powerful …

Characterizing network-level rhythmic dynamics over multiple spatio-temporal scales can
significantly advance our understanding of brain cognitive function and information …

We propose a novel algorithm to extract frequency-band specific and non-linear Granger
causality (Spectral NLGC) connections between components of a multivariate time series …

Granger causality analysis is becoming central for the analysis of interactions between
neural populations and oscillatory networks. However, it is currently unclear whether single …

Intracranial field potentials (FP) are mesoscopic electrical signals that reflect the activity of
neuron populations with high temporal precision. They also provide good spatial resolution …