Conference PaperWe develop two new multivariate statistical dependence measures. The first, based on the Kullback-Leibler distance, results in a single value that indicates the general level of dependence among the random variables. The second, based on an orthonormal series expansion of joint probability density functions, provides more detail about the nature of the dependence. We apply these dependence measures to the analysis of simultaneous recordings made from multiple neurons, in which dependencies are time-varying and potentially information bearing.National Institute of Mental Healt
The final publication is available at link.springer.comWe propose new dependence measures for two re...
Abstract — Assessing dependence between two sets of spike trains or between a set of input stimuli a...
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Statistical dependency between neuronal spike trains forms a basis for information encoding in memor...
A method to estimate from multivariate measurements the dependences within a network of coupled dyna...
Large volumes of neuroscience data comprise multiple, nonstationary electrophysiological or neuroima...
We describe an algorithm to quantify dependence in a multivariate data set. The algorithm is able to...
Medidas de dependência entre séries temporais são estudadas com a perspectiva de evidenciar como dif...
A fundamental problem in neuroscience is determining whether or not particular neural signals are de...
Measuring the dependence of data plays a central role in statistics and machine learning. In this wo...
Two families of dependence measures between random variables are introduced. They are based on the R...
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with em...
In this article, we show that the recently introduced ordinal pattern dependence fits into the axiom...
To understand the functional connectivity of neural networks, it is important to develop simple and ...
<p>Statistical inference on conditional dependence is essential in many fields including genetic ass...
The final publication is available at link.springer.comWe propose new dependence measures for two re...
Abstract — Assessing dependence between two sets of spike trains or between a set of input stimuli a...
AbstractRepeated measurements and multimodal data are common in neuroimaging research. Despite this,...
Statistical dependency between neuronal spike trains forms a basis for information encoding in memor...
A method to estimate from multivariate measurements the dependences within a network of coupled dyna...
Large volumes of neuroscience data comprise multiple, nonstationary electrophysiological or neuroima...
We describe an algorithm to quantify dependence in a multivariate data set. The algorithm is able to...
Medidas de dependência entre séries temporais são estudadas com a perspectiva de evidenciar como dif...
A fundamental problem in neuroscience is determining whether or not particular neural signals are de...
Measuring the dependence of data plays a central role in statistics and machine learning. In this wo...
Two families of dependence measures between random variables are introduced. They are based on the R...
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with em...
In this article, we show that the recently introduced ordinal pattern dependence fits into the axiom...
To understand the functional connectivity of neural networks, it is important to develop simple and ...
<p>Statistical inference on conditional dependence is essential in many fields including genetic ass...
The final publication is available at link.springer.comWe propose new dependence measures for two re...
Abstract — Assessing dependence between two sets of spike trains or between a set of input stimuli a...
AbstractRepeated measurements and multimodal data are common in neuroimaging research. Despite this,...