The spectrum and coherency are useful quantities for characterizing the temporal correlations and functional relations within and between point processes. This article begins with a review of these quantities, their interpretation, and how they may be estimated. A discussion of how to assess the statistical significance of features in these measures is included. In addition, new work is presented that builds on the framework established in the review section. This work investigates how the estimates and their error bars are modified by finite sample sizes. Finite sample corrections are derived based on a doubly stochastic inhomogeneous Poisson process model in which the rate functions are drawn from a low-variance gaussian process. It is fo...
This paper deals with the coherence function in order to study relations between channels, in the co...
Spectra and coherences are standard measures of association within and between time series. These me...
Coherence is a widely used measure for characterizing linear dependence between two time series. Cla...
The spectrum and coherency are useful quantities for characterizing the temporal correlations and fu...
The spectrum and coherency are useful quantities for characterizing the temporal correlations and fu...
The spectrum and coherency are useful quantities for characterizing the temporal correlations and fu...
In neuroscience, it is of key importance to assess how neurons interact with each other as evidenced...
h i g h l i g h t s • Spike-field coherence (SFC) is dependent on spike rate. • Rate-dependence conf...
AbstractCoherence is a fundamental tool in the analysis of neuronal data and for studying multiscale...
The thesis deals with the analysis and modeling of point processes emerging from different experimen...
In a growing class of neurophysiological experiments, the train of impulses (“spikes”) produced by a...
In the last decade-and-a-half, a number of unexpected statistical properties have been found in the ...
A comparison of previously defined spike train synchronization indices is undertaken within a stocha...
Analyzing time series in the frequency domain enables the development of powerful tools for investig...
A comparison of previously defined spike train synchronization indices is undertaken within a stocha...
This paper deals with the coherence function in order to study relations between channels, in the co...
Spectra and coherences are standard measures of association within and between time series. These me...
Coherence is a widely used measure for characterizing linear dependence between two time series. Cla...
The spectrum and coherency are useful quantities for characterizing the temporal correlations and fu...
The spectrum and coherency are useful quantities for characterizing the temporal correlations and fu...
The spectrum and coherency are useful quantities for characterizing the temporal correlations and fu...
In neuroscience, it is of key importance to assess how neurons interact with each other as evidenced...
h i g h l i g h t s • Spike-field coherence (SFC) is dependent on spike rate. • Rate-dependence conf...
AbstractCoherence is a fundamental tool in the analysis of neuronal data and for studying multiscale...
The thesis deals with the analysis and modeling of point processes emerging from different experimen...
In a growing class of neurophysiological experiments, the train of impulses (“spikes”) produced by a...
In the last decade-and-a-half, a number of unexpected statistical properties have been found in the ...
A comparison of previously defined spike train synchronization indices is undertaken within a stocha...
Analyzing time series in the frequency domain enables the development of powerful tools for investig...
A comparison of previously defined spike train synchronization indices is undertaken within a stocha...
This paper deals with the coherence function in order to study relations between channels, in the co...
Spectra and coherences are standard measures of association within and between time series. These me...
Coherence is a widely used measure for characterizing linear dependence between two time series. Cla...