Connectionists: Leaders and followers: Quantifying consistency in spatio-temporal propagation pattern

[Thomas Kreuz]

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 at cnr.it
Webpage: http://www.fi.isc.cnr.it/users/thomas.kreuz/
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