@article{KralemannPikovskijRosenblum2014,
  author    = {Kralemann, Bjoern and Pikovskij, Arkadij and Rosenblum, Michael},
  title     = {Reconstructing effective phase connectivity of oscillator networks from observations},
  series = {New journal of physics : the open-access journal for physics},
  volume    = {16},
  journal   = {New journal of physics : the open-access journal for physics},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1367-2630},
  doi       = {10.1088/1367-2630/16/8/085013},
  pages     = {21},
  year      = {2014},
  abstract  = {We present a novel approach for recovery of the directional connectivity of a small oscillator network by means of the phase dynamics reconstruction from multivariate time series data. The main idea is to use a triplet analysis instead of the traditional pairwise one. Our technique reveals an effective phase connectivity which is generally not equivalent to a structural one. We demonstrate that by comparing the coupling functions from all possible triplets of oscillators, we are able to achieve in the reconstruction a good separation between existing and non-existing connections, and thus reliably reproduce the network structure.},
  language  = {en}
}
@phdthesis{Rosenblum2003,
  author    = {Rosenblum, Michael},
  title     = {Phase synchronization of chaotic systems : from theory to experimental applications},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0000682},
  school      = {Universit{\"a}t Potsdam},
  year      = {2003},
  abstract  = {In einem klassischen Kontext bedeutet Synchronisierung die Anpassung der Rhythmen von selbst-erregten periodischen Oszillatoren aufgrund ihrer schwachen Wechselwirkung. Der Begriff der Synchronisierung geht auf den ber{\"u}hmten niederl{\"a}andischen Wissenschaftler Christiaan Huygens im 17. Jahrhundert zur{\"u}ck, der {\"u}ber seine Beobachtungen mit Pendeluhren berichtete. Wenn zwei solche Uhren auf der selben Unterlage plaziert wurden, schwangen ihre Pendel in perfekter {\"U}bereinstimmung. Mathematisch bedeutet das, daß infolge der Kopplung, die Uhren mit gleichen Frequenzen und engverwandten Phasen zu oszillieren begannen. Als wahrscheinlich {\"a}ltester beobachteter nichtlinearer Effekt wurde die Synchronisierung erst nach den Arbeiten von E. V. Appleton und B. Van der Pol gegen 1920 verstanden, die die Synchronisierung in Triodengeneratoren systematisch untersucht haben. Seitdem wurde die Theorie gut entwickelt, und hat viele Anwendungen gefunden. Heutzutage weiss man, dass bestimmte, sogar ziemlich einfache, Systeme, ein chaotisches Verhalten aus{\"u}ben k{\"o}nnen. Dies bedeutet, dass ihre Rhythmen unregelm{\"a}ßig sind und nicht durch nur eine einzige Frequenz charakterisiert werden k{\"o}nnen. Wie in der Habilitationsarbeit gezeigt wurde, kann man jedoch den Begriff der Phase und damit auch der Synchronisierung auf chaotische Systeme ausweiten. Wegen ihrer sehr schwachen Wechselwirkung treten Beziehungen zwischen den Phasen und den gemittelten Frequenzen auf und f{\"u}hren damit zur {\"U}bereinstimmung der immer noch unregelm{\"a}ßigen Rhythmen. Dieser Effekt, sogenannter Phasensynchronisierung, konnte sp{\"a}ter in Laborexperimenten anderer wissenschaftlicher Gruppen best{\"a}tigt werden. Das Verst{\"a}ndnis der Synchronisierung unregelm{\"a}ßiger Oszillatoren erlaubte es uns, wichtige Probleme der Datenanalyse zu untersuchen. Ein Hauptbeispiel ist das Problem der Identifikation schwacher Wechselwirkungen zwischen Systemen, die nur eine passive Messung erlauben. Diese Situation trifft h{\"a}ufig in lebenden Systemen auf, wo Synchronisierungsph{\"a}nomene auf jedem Niveau erscheinen - auf der Ebene von Zellen bis hin zu makroskopischen physiologischen Systemen; in normalen Zust{\"a}nden und auch in Zust{\"a}nden ernster Pathologie. Mit unseren Methoden konnten wir eine Anpassung in den Rhythmen von Herz-Kreislauf und Atmungssystem in Menschen feststellen, wobei der Grad ihrer Interaktion mit der Reifung zunimmt. Weiterhin haben wir unsere Algorithmen benutzt, um die Gehirnaktivit{\"a}t von an Parkinson Erkrankten zu analysieren. Die Ergebnisse dieser Kollaboration mit Neurowissenschaftlern zeigen, dass sich verschiedene Gehirnbereiche genau vor Beginn des pathologischen Zitterns synchronisieren. Außerdem gelang es uns, die f{\"u}r das Zittern verantwortliche Gehirnregion zu lokalisieren.},
  language  = {en}
}
@article{RosenblumPikovsky2023,
  author    = {Rosenblum, Michael and Pikovsky, Arkady},
  title     = {Inferring connectivity of an oscillatory network via the phase dynamics reconstruction},
  series = {Frontiers in network physiology},
  volume    = {3},
  journal   = {Frontiers in network physiology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2674-0109},
  doi       = {10.3389/fnetp.2023.1298228},
  pages     = {10},
  year      = {2023},
  abstract  = {We review an approach for reconstructing oscillatory networks' undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network's coupling matrix in the first approximation in the coupling strength.},
  language  = {en}
}
@misc{TopcuFruehwirthMoseretal.2018,
  author    = {Top{\c{c}}u, {\c{C}}ağda{\c{s}} and Fr{\"u}hwirth, Matthias and Moser, Maximilian and Rosenblum, Michael and Pikovskij, Arkadij},
  title     = {Disentangling respiratory sinus arrhythmia in heart rate variability records},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {913},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43631},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-436315},
  pages     = {15},
  year      = {2018},
  abstract  = {Objective: Several different measures of heart rate variability, and particularly of respiratory sinus arrhythmia, are widely used in research and clinical applications. For many purposes it is important to know which features of heart rate variability are directly related to respiration and which are caused by other aspects of cardiac dynamics. Approach: Inspired by ideas from the theory of coupled oscillators, we use simultaneous measurements of respiratory and cardiac activity to perform a nonlinear disentanglement of the heart rate variability into the respiratory-related component and the rest. Main results: The theoretical consideration is illustrated by the analysis of 25 data sets from healthy subjects. In all cases we show how the disentanglement is manifested in the different measures of heart rate variability. Significance: The suggested technique can be exploited as a universal preprocessing tool, both for the analysis of respiratory influence on the heart rate and in cases when effects of other factors on the heart rate variability are in focus.},
  language  = {en}
}
@article{TopcuFruehwirthMoseretal.2018,
  author    = {Top{\c{c}}u, {\c{C}}ağda{\c{s}} and Fr{\"u}hwirth, Matthias and Moser, Maximilian and Rosenblum, Michael and Pikovskij, Arkadij},
  title     = {Disentangling respiratory sinus arrhythmia in heart rate variability records},
  series = {Physiological Measurement},
  volume    = {39},
  journal   = {Physiological Measurement},
  number    = {5},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {0967-3334},
  doi       = {10.1088/1361-6579/aabea4},
  pages     = {12},
  year      = {2018},
  abstract  = {Objective: Several different measures of heart rate variability, and particularly of respiratory sinus arrhythmia, are widely used in research and clinical applications. For many purposes it is important to know which features of heart rate variability are directly related to respiration and which are caused by other aspects of cardiac dynamics. Approach: Inspired by ideas from the theory of coupled oscillators, we use simultaneous measurements of respiratory and cardiac activity to perform a nonlinear disentanglement of the heart rate variability into the respiratory-related component and the rest. Main results: The theoretical consideration is illustrated by the analysis of 25 data sets from healthy subjects. In all cases we show how the disentanglement is manifested in the different measures of heart rate variability. Significance: The suggested technique can be exploited as a universal preprocessing tool, both for the analysis of respiratory influence on the heart rate and in cases when effects of other factors on the heart rate variability are in focus.},
  language  = {en}
}