[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. http://www.neurospec.org/ An accompanying journal article is published in the Journal of Integrative Neuroscience: "Nonparametric directionality measures for time series and point process data", J. Integr. Neurosci., 14, 253-277 (2015). DOI: 10.1142/S0219635215300127 http://dx.doi.org/10.1142/S0219635215300127 Abstract 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 YORK YO10 5DD, UK. E-Mail: david.halliday@york.ac.uk