TY - JOUR
T1 - Leaders and followers
T2 - Quantifying consistency in spatio-temporal propagation patterns
AU - Kreuz, Thomas
AU - Satuvuori, Eero
AU - Pofahl, Martin
AU - Mulansky, Mario
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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 Nio sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.
AB - 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 Nio sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.
KW - climate science
KW - data analysis
KW - neuroscience
KW - simulated annealing
KW - spatio-temporal propagation
KW - spike train order
KW - SPIKE-order
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UR - http://www.scopus.com/inward/citedby.url?scp=85018318555&partnerID=8YFLogxK
U2 - 10.1088/1367-2630/aa68c3
DO - 10.1088/1367-2630/aa68c3
M3 - Article
AN - SCOPUS:85018318555
SN - 1367-2630
VL - 19
JO - New Journal of Physics
JF - New Journal of Physics
IS - 4
M1 - 043028
ER -