Dear all, may I kindly draw your attention to our paper on the new multivariate directional measure *SPIKE-Order*. In this paper we propose a new approach to quantify consistency of spatio-temporal propagation patterns in sequences of discrete events (e.g. spike trains). This includes a sorting from leader to follower. As usual we show some applications to neurophysiological data. *Leaders and followers: Quantifying consistency in spatio-temporal propagation pattern <http://iopscience.iop.org/article/10.1088/1367-2630/aa68c3/meta>* Thomas Kreuz, Eero Satuvuori, Martin Pofahl and Mario Mulansky New J. Phys., *19*, 043028 (2017). Abstract: Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a *synfire pattern*. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed *SPIKE-order* and *spike train order*, that define the *synfire indicator* value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality. Implementations are provided online in three free code packages called SPIKY <http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/SPIKY.html> (Matlab GUI), PySpike <http://mariomulansky.github.io/PySpike/>(Python library) and, most recently, cSPIKE <http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/cSPIKE.html>(Matlab command line with MEX-files). Best regards, Thomas Kreuz PS: Three further recent articles: *Measures of spike train synchrony for data with multiple time scales <http://www.sciencedirect.com/science/article/pii/S0165027017301619>* Eero Satuvuori, Mario Mulansky, Nebojsa Bozanic, Irene Malvestio, Fleur Zeldenrust, Kerstin Lenk, Thomas Kreuz JNeurosci Methods *287*, 25 (2017). Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset. New method In data containing multiple time scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time scales and a more adaptive approach is needed. Here we propose the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing. Results The adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains. Comparison with existing methods We find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization. Conclusions With these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance). *Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.022203>* Irene Malvestio, Thomas Kreuz, Ralph G Andrzejak Physical Review E *96*, 022203 (2017). The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data. *SPIKE-order <http://www.scholarpedia.org/article/SPIKE-order>* Thomas Kreuz, Eero Satuvuori, Mario Mulansky Scholarpedia, *12*(7):42441 (2017). -- Institute for complex systems, CNR Via Madonna del Piano 10 50119 Sesto Fiorentino (Italy) Tel: +39-349-0748506 Email: thomas.kreuz@cnr.it Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/