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You are here: FRIAS Scientific Staff Scientific Staff Archive Linda Sommerlade

Linda Sommerlade

Freiburg Center for Data Analysis and Modeling
PhD Student with External Senior Fellow Celso Grebogi

Room Room 113, FDM, Eckerstr. 1, 79104 Freiburg
Phone +49 (0)761 203-7709
Fax +49 (0)761 203-7700


  • since March 2009: PhD Student at the Albert-Ludwigs-University Freiburg, Germany
  • October 2005 - March 2009: Student research assistant in the group of Prof. Dr. Jens Timmer
  • October 2005 - March 2009: Advanced study period at the Albert-Ludwigs-University Freiburg, Germany (Diploma in Physics)
  • October 2003 - September 2005: Basic studies in Physics at the Julius-Maximilian-University Würzburg, Germany



Selected Publications

    • L. Sommerlade, F. Amtage, O. Lapp, B. Hellwig, C.H. Lücking, J. Timmer, B. Schelter. On the estimation of the direction of information flow in networks of dynamical systems tremor. J. Neurosci. Meth.196, 2011, 182-189
    • L. Sommerlade, M. Eichler, M. Jachan, K. Henschel, J. Timmer, B. Schelter. Estimating causal dependencies in networks of nonlinear stochastic dynamical systems. Phys. Rev. E 80, 2009, 051128
    • L. Sommerlade, K. Henschel, J. Wohlmuth, M. Jachan, F. Amtage, B. Hellwig, C.H. Lücking, J. Timmer, B. Schelter. Time-variant estimation of directed influence during Parkinsonian tremor. J. Physiol.103, 2009, 348-352


    FRIAS Research Project

    Investigations of the behaviour of dynamical networks using a direct as well as an inverse approach

    Complex systems consist of many interacting components, characterised by the presence of emergent behaviour, and whose dynamics are ubiquitous in the life sciences. The applications of fundamental concepts from the theory of nonlinear complex dynamical systems are not only central for understanding cell biology oriented systems biology but it is also essential for the understanding of a range of problems from neurosciences to environmental systems. First principle modelling, inference, as well as control of such complex systems have become increasingly important. Networks of interacting nodes, each with their own dynamics, are one of the main mathematical tools for the description of complex systems. Depending on the particular application, the dynamical behaviour of the nodes can be anything from binary, such as in the Kauffman networks for the description of genetic or metabolic pathways, to deterministic or stochastic and even chaotic networks such as the ones in some neuronal models.

    This project proposes to understand the behaviour of dynamical networks from two complementary directions, namely, by using a direct as well as an inverse approach.