<div dir="ltr"><div>[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. <br><br><a href="http://www.neurospec.org/" target="_blank">http://www.neurospec.org/</a><br><br>An accompanying journal article is published in the Journal of Integrative Neuroscience:<br>"Nonparametric
directionality measures for time series and point process data", J.
Integr. Neurosci., 14, 253-277 (2015). DOI: 10.1142/S0219635215300127<br><br><a href="http://dx.doi.org/10.1142/S0219635215300127" target="_blank">http://dx.doi.org/10.1142/S0219635215300127</a><br><br></div>Abstract<br>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.<br><br><span class=""><font color="#888888">- <br>
David Halliday<br>
Department of Electronics<br>
University of York<br>
YORK YO10 5DD, UK.<br></font></span><span class=""><font color="#888888"><br>E-Mail: <a href="mailto:david.halliday@york.ac.uk" target="_blank">david.halliday@york.ac.uk</a></font></span><br></div>