Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Projects

Our research focuses on:

Dynamics of Modern Power Grids

Switching our current energy supply to renewable sources poses one of the greatest technological and social challenges of humankind. A successful transition in particular requires an intelligent upgrade of the current electric power grid. So-called 'smart grids' may provide part of the solution by enabling the transmission of demand and supply information across the grid online, thereby adapting energy production and distribution, and thus aiming to control the entire grid. However, stable operation as well as failures on large scales already today are consequences of the collective dynamics of the power grid and are often caused by non-local mechanisms. We thus urgently need to understand the intrinsic network dynamics on the large scale to complement partial solutions of control engineering and to be able to develop efficient strategies for operating the future grid. We thus develop and analyze appropriate coarse-scale models of future power grids with an emphasis on increasingly distributed demand and supply. First results show several intriguing features. For instance, the addition of new transmission lines may *destabilize* power grid operation (via Braess paradox that we identified in oscillator networks). In addition, replacing the few large power plants by many small and distributed ones may stabilize grid operation, at least in the stationary (short-time) regime.

Control and Selforganization for Autonomous Robots

How can distributed or autonomous systems control themselves to function properly? We are developing a novel line of research: Advancing the theory of chaos control we strive to bring it to applications for making autonomous robots more versatile and more self-organized

Correlated weakly disordered systems

Particles as well as waves flowing through a weak disorder potential show surprising branching effects. This has most notably been observed in two-dimensional electron gases, but is a phenomenon of much wider applicability. We study the formation of the caustics responsible for the branching of the flow.

Complex Disordered Systems: Statistical Physics and Symbolic Computation

Many particles interacting nonlinearly often give rise to very complex behavior. This is true even for apparently simple systems, such as those in thermal equilibrium. For instance, particles with a spin that are anti-ferromagnetically coupled may give rise to positive ground state entropy, an exception to the third law of thermodynamics. Investigating such complex ground states immediately leads us to hard enumeration problems in graph theory and computer science. Here we try to understand basic features of complex macroscopic states and in parallel develop tools for analytically and computationally addressing large system with complex ground states and related graph theoretical problems.

Nonlinear Methods for Detecting Timing Relations in Neural Data

Making sense of huge neural data sets that contain spikes as well as temporally more coarse information constitutes a challenging task of current research. This is even more so as. for instance, the number of units possible to recorded from simulataneously, increases at a rapid pace. In this project we are developing novel methods of nonlinear time series analysis to relate dynamical quantities of neural activity on different temporal and spatial scales. We currently focus on relating the often precisely timed occurrence of spikes to the temporal evolution of local field potentials and low frequency oscillations via modern phase analysis techniques.

Structure, Dynamics and Function of Complex Networks

How does the interaction topology of a complex network control its dynamics? Can we infer information about how a network is connected from dynamics measurements only? We address theoretical and practical aspects of such questions using mathematical modeling studies for general network dynamical systems and neural networks in particular.

Theory of Precise Timing in Spiking Neural Networks

Coordinated patterns of precisely timed activity is a key ingredient for neural information processing. This project investigates the theoretical fundamentals underlying the mechanisms for generating precisely timed spikes in complex neural networks.

Avalanches in Bose-Einstein Condensates

The dynamics of Bose-Einstein condensates in leaky optical lattices is studied (in the mean field limit). For some critical values of the interatomic interaction strength, the current of atoms leaving the trap exhibits avalanches that follow a power-law distribution and indicate the existence of a novel phase transition.

Disorder in Quantum Systems with Antilinear Symmetries

We study the influence of disorder on the pseudo-hermitian phase of (generalized) PT-symmetric systems.

Complex call sequences in social whale communication

How do social animals communicate? Social whales may be one of the best fit animal model to study complex vocal communication patterns. We are interested in developing an automated classification system for vocalizations of killer whales and pilot whales. Very loosely speaking one can think of this as developing a "speech"-recognition system for whale vocalizations. Another, more general aim of this project is to characterize whale vocalizations in terms of information theoretic measures and to compare them to human languages.

Fluctuations in human musical rhythms

Music generated by computers and rhythm machines sometimes sounds unnatural. One reason for this is the absence of small inaccuracies that are part of every human activity. Professional audio software therefore offers a so-called humanizing technique, by which the regularity of musical rhythms can be randomized to some extent. But what exactly is the nature of the inaccuracy in human musical rhythms? Studying this question for the first time, we found that the temporal rhythmic fluctuations exhibit scale-free long-range correlations, i.e., a small rhythmic fluctuation at some point in time does not only influence fluctuations shortly thereafter, but even after tens of seconds. While this characterization is relevant for neurophysiological mechanisms of timing, it also leads to a novel concept for humanizing musical sequences. Comparing with conventionally humanized versions listeners showed a high preference for long-range correlated humanized music over uncorrelated humanized music.

Anomalous Diffusion

If the time-evolution of the mean-squared displacement of some quantity is non-linear, the system is said to exhibit anomalous diffusion. The underlying mechanisms leading to such anomalous diffusion can be multifold. Our group focuses on processes whose anomalous behavior is due to heavy-tailed distributions of either the waiting time distribution between the displacements or of the displacements themselves.

Self-Organized Criticality in the Activity Dynamics of Neural Networks

Neural networks display characteristics of critical dynamics in the neural activities as theoretically predicted. The power-law statistics for the size of avalanches of neural activity was confirmed in real neurons, where the critical behavior is re-approached even after a substantial perturbation of the parameters of the system. These findings provide evidence for the presence of self-organized criticality (SOC). We study neural network models that exhibit power-law statistics with realistic synaptic mechanisms, such as synaptic depression.

Criticality and Self-Organization in Behavioral Control

The biological function of self-organized criticality is much less understood than the physical mechanisms behind this phenomenon. Critical dynamics seems beneficial to living beings and it is known to bring about optimal computational capabilities, optimal transmission and storage of information, and sensitivity to sensory stimuli. We are interested in the developmental aspects of motor behavior in animals which we study in biomorphic autonomous robots.

Computational Modeling in Cognitive Psychology

This project divided into two subgroups: 'Aging effects in selective attention' and 'Dynamic adaptation in decoding temporal information' that both develop computational models to explain effects in cognitive psychology in a more detailed way than usual psychological theories can. These models base on knowledge about biological processes and insights via abstract modeling in computational neuroscience yielding quantitative model predictions that can directly be tested in psychological experiments.