Copula is an important tool for modeling neural dependence. Recent work on copula has
been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint …

The goal of this paper is to develop a measure for characterizing complex dependence
between time series that cannot be captured by traditional measures such as correlation and …

Recent technological advances, which allow for simultaneous recording of spikes and local
field potentials (LFPs) at multiple sites in a given cortical area or across different areas, have …

The question as to the role that correlated activity plays in the coding of information in the
brain continues to be one of the most important in neuroscience. One approach to …

The activities of individual neurons in cortex and many other areas of the brain are often well
described by Poisson distributions. Unfortunately, there is no simple joint Poisson …

One of the main goals of current systems neuroscience is to understand how neuronal
populations integrate sensory information to inform behavior. However, estimating stimulus …

In order to evaluate the importance of higher-order correlations in neural spike count codes,
flexible statistical models of dependent multivariate spike counts are required. Copula …

Tools for characterizing non-linear spectral dependence between spontaneous brain
signals are developed, based on the use of parametric copula models (both bivariate and …

Simultaneous spike-counts of neural populations are typically modeled by a Gaussian
distribution. On short time scales, however, this distribution is too restrictive to describe and …

In systems neuroscience, it is becoming increasingly common to record the activity of
hundreds of neurons simultaneously via electrode arrays. The ability to accurately measure …