Inferring structure of pulse-coupled oscillatory networks from data
Early Stage Researcher: Rok Cestnik
Principle Investigators: Michael Rosenblum (Potsdam, UP – major institution), Andreas Daffertshofer and Bob van Dijk (Amsterdam, VUA – partner institution)
Extraction of information about oscillatory networks from time series is an important problem with a number of applications in life sciences. The task can be formulated as follows: we try to recover the network structure from multichannel measurements obtained from passive observations of the system. In particular, we are interested in the directed connectivity of the network. In our previous work we have developed an approach, which allows us to tackle this issue in the case of oscillatory processes that allow for conventional phase estimation.
The goal of the current project is to extend this approach to the case of pulse-coupling and pulse-type time series from continuously coupled oscillators. We intend to develop algorithms to recover the strength and direction of interactions between oscillators from point processes and test these algorithms in synthetic data from oscillatory models, e.g., from coupled spiking neurons. We also seek to apply the to-be-developed techniques to experimental time series.
B. Kralemann, A. Pikovsky, and M. Rosenblum, Reconstructing effective phase connectivity of oscillator networks from observations, New Journal of Physics, 16, 085013, 2014
B. Kralemann, M. Frühwirth, A. Pikovsky, M. Rosenblum, T. Kenner, J. Schaefer, and M. Moser, In vivo cardiac phase response curve elucidates human respiratory heart rate variability, Nature Communications, 4, p. 2418, 2013
B. Kralemann, L. Cimponeriu, M.G. Rosenblum, A.S. Pikovsky, and R. Mrowka, Phase dynamics of coupled oscillators reconstructed from data, Physical Review E, 77, p. 066205, 2008
All papers can be downloaded here
Software implementation can be found here