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<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">Dear all,<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">may I kindly
draw your attention to our paper on the new multivariate directional measure <b>SPIKE-Order</b>. 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.<span></span></span></p>
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Cambria"><span> </span></span></b></p>
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Cambria"><a href="http://iopscience.iop.org/article/10.1088/1367-2630/aa68c3/meta">Leaders
and followers: Quantifying consistency in spatio-temporal propagation pattern</a><span></span></span></b></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">Thomas
Kreuz, Eero Satuvuori, Martin Pofahl and Mario Mulansky<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:black;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">New J. Phys., <b>19</b>, 043028 (2017). <span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:black;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:black;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Abstract:</span><span style="font-size:11pt;font-family:Cambria"><span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:black;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:rgb(51,51,51)">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 <i><span style="border:1pt none windowtext;padding:0cm">synfire pattern</span></i>. 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 <i><span style="border:1pt none windowtext;padding:0cm">SPIKE-order</span></i> and <i><span style="border:1pt none windowtext;padding:0cm">spike train order</span></i>,
that define the <i><span style="border:1pt none windowtext;padding:0cm">synfire indicator</span></i> 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.<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:rgb(51,51,51)"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)">Implementations are provided online in three free code
packages called <a href="http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/SPIKY.html">SPIKY</a> (Matlab GUI), <a href="http://mariomulansky.github.io/PySpike/">PySpike </a><a name="bfn0015"></a>(Python library) and, most recently, <a href="http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/cSPIKE.html">cSPIKE </a>(Matlab
command line with MEX-files).<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">Best
regards,<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">Thomas Kreuz<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">PS: Three
further recent articles:<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria"><span> </span></span></p>
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Cambria"><a href="http://www.sciencedirect.com/science/article/pii/S0165027017301619">Measures
of spike train synchrony for data with multiple time scales</a><span></span></span></b></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria">Eero
Satuvuori, Mario Mulansky, Nebojsa Bozanic, Irene Malvestio, Fleur Zeldenrust,
Kerstin Lenk, Thomas Kreuz<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:black;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">JNeurosci Methods <b>287</b>, 25 (2017).<span></span></span></p>
<h3 style="margin-top:9pt;margin-bottom:6pt;line-height:15pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80);font-weight:normal">Background<span></span></span></h3>
<p style="margin-top:0cm;margin-bottom:12pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)">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.<span></span></span></p>
<h3 style="margin-top:9pt;margin-bottom:6pt;line-height:15pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80);font-weight:normal">New method<span></span></span></h3>
<p style="margin-top:0cm;margin-bottom:12pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)">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.<span></span></span></p>
<h3 style="margin-top:9pt;margin-bottom:6pt;line-height:15pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80);font-weight:normal">Results<span></span></span></h3>
<p style="margin-top:0cm;margin-bottom:12pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)">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.<span></span></span></p>
<h3 style="margin-top:9pt;margin-bottom:6pt;line-height:15pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80);font-weight:normal">Comparison with existing methods<span></span></span></h3>
<p style="margin-top:0cm;margin-bottom:12pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)">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.<span></span></span></p>
<h3 style="margin-top:9pt;margin-bottom:6pt;line-height:15pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80);font-weight:normal">Conclusions<span></span></span></h3>
<p style="margin-top:0cm;margin-bottom:12pt"><span style="font-size:11pt;font-family:Cambria;color:rgb(80,80,80)">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).<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria"><span> </span></span></p>
<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><b><span style="font-size:11pt;font-family:Cambria;color:black"><a href="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.022203">Robustness
and versatility of a nonlinear interdependence method for directional coupling
detection from spike trains</a><span></span></span></b></p>
<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:11pt;font-family:Cambria;color:black">Irene Malvestio, Thomas Kreuz, Ralph G Andrzejak<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;color:black">Physical
Review E <b>96</b>, 022203 (2017).<span></span></span></p>
<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:11pt;font-family:Cambria;color:black"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">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 <span style="border:1pt none windowtext;padding:0cm">L</span>. 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 <span style="border:1pt none windowtext;padding:0cm">L</span> is versatile and robust to
various types of noise, and thus suitable for application to experimental data.<span></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span> </span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Cambria;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span> </span></span></p>
<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><b><span style="font-size:11pt;font-family:Cambria;color:black"><a href="http://www.scholarpedia.org/article/SPIKE-order">SPIKE-order</a></span></b><span style="font-size:11pt;font-family:Cambria;color:black"><br>
Thomas Kreuz, Eero Satuvuori, Mario Mulansky<span></span></span></p>
<p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:11pt;font-family:Cambria;color:black">Scholarpedia, <b>12</b>(7):42441 (2017).<span></span></span></p>
<div><br></div><div><br></div><div><br></div>-- <br><div class="gmail_signature"><div dir="ltr">Institute for complex systems, CNR<br>Via Madonna del Piano 10<br>50119 Sesto Fiorentino (Italy)<br>Tel: <span></span>+39-349-0748506<br>Email: <span></span><a href="mailto:thomas.kreuz@cnr.it" target="_blank">thomas.kreuz@cnr.it</a><br>Webpage: <a rel="nofollow" href="http://www.fi.isc.cnr.it/users/thomas.kreuz/" target="_blank">http://www.fi.isc.cnr.it/users/thomas.kreuz/</a></div></div>
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