Brain information processing likely relies on cooperative interactions between neural
populations at multiple scales. Growing evidence suggests that network oscillations, as …

Multi-layered brain structures contain canonical microcircuits that specialize in region-
specific function. Information flow across the layers is typically inferred using multivariate …

ABSTRACT IMPACT: Brain networks can be explored by delivering brief pulses of electrical
current in one area while measuring responses in other areas, and this describes an open …

Attempts to identify causal interactions in multivariable biological time series (eg, gene data,
protein data, physiological data) can be undermined by the confounding influence of …

The observation of physical phenomena often goes through the recording of discrete time
series of events, that can be represented with marked point processes. The robust …

Distinct dynamics in different cortical layers are apparent in neuronal and local field potential
(LFP) patterns, yet their associations in the context of laminar processing have been …

To fully understand the computational properties of a neural network, it is crucial to know the
network's connectivity. Unfortunately, connectivity inference using easily accessible data …

Neurons in the brain form complicated networks through synaptic connections. Traditionally,
functional connectivity between neurons has been analyzed using simple metrics such as …

In recent years, the study of the human connectome (ie of statistical relationships between
non spatially contiguous neurophysiological events in the human brain) has been …

Background The assessment of directed functional connectivity from neuronal data is
increasingly common in neuroscience by applying measures based in the Granger causality …