[Comp-neuro] Software for Non-parametric directionality analysis

David Halliday david.halliday at york.ac.uk
Tue Jul 14 16:56:11 CEST 2015

[Apologies for cross posting] MATLAB software to implement non-parametric
directionality analysis for spike-train and time-series data is available
for free download from the NeuroSpec archive. A user guide and
demonstration scripts are included.


An accompanying journal article is published in the Journal of Integrative
"Nonparametric directionality measures for time series and point process
data", J. Integr. Neurosci., 14, 253-277 (2015). DOI:


The need to determine the directionality of interactions between neural
signals is a key requirement for analysis of multichannel recordings.
Approaches most commonly used are parametric, typically relying on
autoregressive models. A number of concerns have been expressed regarding
parametric approaches, thus there is a need to consider alternatives. We
present an alternative nonparametric approach for construction of
directionality measures for bivariate random processes. The method combines
time and frequency domain representations of bivariate data to decompose
the correlation by direction. Our framework generates two sets of
complementary measures, a set of scalar measures, which decompose the total
product moment correlation coefficient summatively into three terms by
direction and a set of functions which decompose the coherence summatively
at each frequency into three terms by direction: forward direction, reverse
direction and instantaneous interaction. It can be undertaken as an
addition to a standard bivariate spectral and coherence analysis, and
applied to either time series or point-process (spike train) data or
mixtures of the two (hybrid data). In this paper, we demonstrate
application to spike train data using simulated cortical neurone networks
and application to experimental data from isolated muscle spindle sensory
endings subject to random efferent stimulation.

David Halliday
Department of Electronics
University of York

E-Mail: david.halliday at york.ac.uk
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