(Einladender: Prof. Dr. H. Wagner)
Prof. Dr. A. Daffertshofer
Faculty of Human Movement Sciences , University Amsterdam
Transient motor behavior and synchronization in the human cortex - what can we learn from nonlinear dynamics?

Bimanual coordination requires the functional integration of various cortical, subcortical, spinal, and peripheral neural structures. How is this integration accomplished? Answering this question does not only add to our understanding of motor control but also helps to unravel more general mechanisms underlying the information transfer across the cortex and beyond. To do so, however, experimental protocols and accompanying mathematical descriptions need to tackle transient behavior in addition to the conventionally addressed steady state performance. The focus on transient switches allows for reducing mathematical descriptions to that of low-dimensional systems by means of a separation of time scales (center manifold approach) irrespective of the complexity of the dynamics under study. This applies to behavioral models, to models of macroscopic cortical activity, and to more local neural mass models. Seminal empirical findings will be outlined as regards movement coordination, isolated switches between steady states, and a permanent loss of stability. To explicitly study neural information transfer, some more recent findings of encephalographic recordings during such cascades of behavioral states will be summarized. For the latter, the dynamics of phases of oscillatory neural populations will be explicated (keyword: Kuramoto network) and linked to beta band oscillations whose envelope characteristics resemble the loss of stability of coordinated rhythmic movements.

(Einladender: Dr. O. Kamps)
Prof. Dr. Thomas Guhr
Fakultät fuer Physik, Universität Duisburg-Essen
Credits and the Instability of the Financial System: a Physicist's Point of View

In the last fifteen to twenty years, a growing number of physicists became interested in the economy, particularly
in the financial system. "Econophysics" emerged as a new field of research. I discuss this development
in general and then turn to an issue which continues to hit the news: credit contracts, their risk, and the
obvious severe instabilites caused for the world economy as a whole.

It is often claimed, particularly by financial institutions, that the risk involved with credits can be substantially
lowered by diversification, i.e. by distributing it. I use a "microscopic" model to show that this very concept
is deeply flawed. The study is carried out with numerical simulations, but also semi-analytically by employing
random matrices which are a powerful tool in the theory of complex and chaotic systems.

I start from scratch! - Background in economics is not needed!

(Einladender: Prof. Dr. C Engwer)
Dr. Nikolaus Kepper
DKFZ & BioQuant Center
3D modeling and computer simulations of chromatin at different scales
The DNA in human cell nuclei has a diameter of ~2 nm and a length of ~2 m. In the first compaction step, DNA forms together with proteins the nucleosome, which then forms the chromatin. The spatial organization of chromatin is a central factor for controlling the DNA access of protein factors involved in transcription, DNA replication and repair. In diploid human cell nuclei DNA / chromatin is divided into 46 chromosomes. Here, three spatial models will be used to answer questions at different scales of DNA / chromatin organization. With all atom molecular dynamic simulations the interactions between DNA and proteins in the nucleosome were analyzed. Based on nucleosomes and connecting linker DNA, a coarse grain model was developed, combined with a Metropolis Monte Carlos algorithm. This model was used to analyze descriptions of more active euchromatin and more condensed heterochromatin and interpret force extension experiments. A more coarse grain model using Brownian Dynamics simulation technique was used to simulate dynamics in the cell nucleus and simulate the compaction of genes in the context of chromosome territories.

(Einladender: Dr. O. Kamps)
Prof. Dr. Martin O.W. Greiner
Department of Engineering and Department of Mathematics, Aarhus University
A 100% renewable power system in Europe
Todays overall macro energy system based on fossil and nuclear resources will transform into a future system dominantly relying on fluctuating renewable resources. At the moment it is not really clear what will be the best transitional pathway between the current and the future energy system. In this respect it makes sense to think backwards, which means in a first step to get a good functional understanding of fully renewable energy systems and then in a second step bridge from there to todays energy system. Based on state-of-the-art high-resolution meteorological and electrical load data, simple spatio-temporal modelling, solid time-series analysis and the physics of complex networks, fundamental properties of a fully renewable pan-European power system are determined. Amongst such characteristics are the optimal mix of wind and solar power generation, the optima combination of storage and balancing, the optimal extension of the transmission network, as well as the optimal ramp down of fossil and nuclear power generation during the transitional phase. These results indicate that the pathways into future energy systems will be driven by an optimal systemic combination of technologies, and that economy and markets have to follow technology.

(Einladender: Prof. Dr. M. Ohlberger)
PD Dr.habil. Thomas R. Knösche
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
Neural Mass Modelling – a Tool for Linking Neurophysiology, Brain Measurements and Behavior
The amazing complexity of human behavior rests, to a large extent, on the interplay between myriads of neurons and other nervous structures. In order to elucidate that link, biologically realistic generative models for electrophysiological (e.g., EEG and MEG) as well as metabolic (e.g., functional MRI) brain data are needed. Unfortunately, the sheer quantitative complexity of the human nervous system renders a detailed, single-neuron based, modelling approach in many cases prohibitive. Neural mass modelling (NMM) is a way to build fairly parsimonious models of neural circuits, which still bear a considerable degree of biological realism, in that their parameters have biological meanings. Entire populations of neurons that are similar in terms of their spatial locations, physiological properties and connectivity are lumped together and described by a mean-field model based on non-linearly coupled ODEs. Interconnected networks of such populations or neural masses can be used to account for observed brain dynamics as well as neurological and psychological phenomena.
In this presentation I will first briefly review the the mathematics of NMMs and describe their main alternatives. I will mainly focus of the model proposed by Jansen and Rit (1995), which is based on current-based synapses. Second, I will present data demonstrating the dynamic repertoire of simple local cortical circuits of NMMs and show that a model comprising as little as three interconnected masses is capable of generating surprisingly complex dynamics that even matches experimental observations. Third, I will discuss ways to describe more extended networks and show how spatio-temporal phenomena can be accounted for. In this context, I will also discuss how structural brain imaging, in particular diffusion MRI, can be used to specify such models. Fourth, I will discuss the issue of inversely specify the parameters of such networks, in particular within the framework of dynamic causal modelling. Lastly, I give a brief account of possibilities to improve the biological realism of the model without overly increasing its complexity, including conductance-based (instead of current-based) synapses, incorporation of astrocytes, gap junctions, various neurotransmitter-receptor systems, etc., and more detailed local intracortical connectivity models.

(Einladender: Prof. Dr. S. Dereich)
Prof. Dr. Klaus Ritter
TU Kaiserslautern
Multi-level Monte Carlo for Approximation of Distribution Functions and an Application to AF4
The multi-level Monte Carlo approach is a powerful variance reduction technique, which is applied, in particular, in the context of stochastic differential equations. While the standard task is to compute the expectation of a real-valued functional on the path space, we discuss how to approximate its distribution function or density on a compact interval in this talk. We illustrate the basic idea of multi-level Monte Carlo, and we establish upper bounds for the error of suitable algorithms. Moreover, we briefly discuss an application to asymmetric flow field flow fractionation (AF4 ), which is a process engineering technique for the separation of nano particles. Mathematically, this leads to the study of exit times of reflected diffusions. Joint work with Mike Giles (Oxford Univ.), Oleg Iliev (Fraunhofer ITWM, Kaiserslautern), and Tigran Nagapetyan (Fraunhofer ITWM, Kaiserslautern).

15.07.2013 (Sondertermin)
(Einladender: Prof. Dr. S. Linz)
Dr. Stefan Facsko
Institut für Ionenstrahlphysik und Materialforschung, Helmholtz-Zentrum Dresden-Rossendorf
Periodic Patterns on Ge surfaces induced by low energy ion irradiation.
Low energy ion irradiation induces the formation of periodic surface patterns. These structured surfaces exhibit periodicities in the range of a few tens to hundreds of nanometers and are promising templates for producing nanostructured thin films [1]. Periodic ripple patterns with wave vector parallel to the ion beam direction are observed frequently for ion irradiation at incidence angles between 50° and 70° to the surface normal [2]. At normal incidence dot or hole patterns with hexagonal symmetry are observed only under special irradiation conditions [3].

We studied the formation of hexagonally arranged hole patterns on Ge(001) surfaces induced by irradiation with a scanned focused Ga+ ion beam (FIB) at normal incidence. Hole patterns with characteristic length of about 50 nm are observed in a narrow energy range of 4 - 6 keV (Fig. 1a). Hole patterns induced by FIB irradiations were compared to broad beam Ga+ and Ge+ irradiations with the same ion energy. No differences were found demonstrating that FIB irradiations with a large overlap of the scanned beam are identical to conventional broad beam irradiations.

Furthermore, ion induced pattern formation on Ge surfaces with 1 keV Ar+ at normal incidence and higher temperature was studied. Similar to the case of ion irradiated crystalline metal surfaces on the crystalline Ge surface a new instability appears at higher temperature due to the Ehrlich-Schwoebel barrier. In this case, we observe regular checkerboard or hole patterns with the symmetry of the patterns reflecting the crystal structure of the irradiated surface (Fig. 1b).


Figure 1: a) Scanning electron microscope (SEM) image of a hole pattern on Ge (001) surface induced by irradiation with a scanned focused 5 keV Ga+ ion beam. b) Atomic force microscope image of a checkerboard pattern on Ge (001) irradiated with 1 keV Ar+ at normal incidence and 260°C.

[1] J. Fassbender, T. Strache, M.O. Liedke, D. Marko, S. Wintz, K. Lenz, A. Keller, S. Facsko, I. Monch, J. McCord, New Journal of Physics 11, 125002 (2009).

[2] W.L. Chan and E. Chason, J. Appl. Phys. 101, 121301 (2007).

[3] M. Fritzsche, A. Muecklich, S. Facsko, Appl. Phys. Lett. 100, 223108 (2012).

(Einladende: Prof. Dr. C. Denz)
Prof. Dr. Joachim Kurtz
Universität Münster
Host-parasite coevolution and immunological memory – An example from modern research on evolution
Parasitism is one of the strongest evolutionary forces. According to the so-called Red Queen Hypothesis, there may be a constant co-evolutionary arms race of adaptations and counter-adaptations of parasites and hosts. Parasites with their often shorter generation times and more flexible genotypes have an advantage and may be able to evolve faster than their hosts. In this process, the increased phenotypic plasticity of hosts provided by their adaptive immune system may help them to balance this disadvantage. Adaptive immunity provides the ability to adapt to parasites during the lifetime of an organism. Immunological memory is the hallmark of the adaptive immune system of vertebrates. However, what about invertebrate species, which are also confronted with fast evolving parasites? Given that some invertebrates are quite long-lived and may encounter similar parasites and pathogens repeatedly over their lifetime, we should expect selection for immune memory also in invertebrates. Invertebrates do not possess homologues of the genes that are crucial to the vertebrate adaptive immune system. Nevertheless, in the last few years, evidence is accumulating from a number of invertebrate taxa for memory-like phenomena in their defense against parasites and pathogens. For some invertebrates, the underlying molecular and immunological processes are beginning to become clearer, but there are still many open questions. We currently use infection assays and experimental evolution approaches to address questions of host-parasite coevolution and the evolution of immunological memory in the Red Flour Beetle Tribolium castaneum and its bacterial microparasite Bacillus thuringiensis.

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