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Our every-day experience is connected with different acoustical noise or music. Usually noise plays the role of nuisance in any communication and destroys any order in a system. Similar optical effects are known: strong snowing or raining decreases quality of a vision. In contrast to these situations noisy stimuli can also play a positive constructive role, e.g. a driver can be more concentrated in a presence of quiet music. Transmission processes in neural systems are of especial interest from this point of view: excitation or information will be transmitted only in the case if a signal overcomes a threshold. Dr. Alexei Zaikin from the Potsdam University studies noise-induced phenomena in nonlinear systems from a theoretical point of view. Especially he is interested in the processes, in which noise influences the behaviour of a system twice: if the intensity of noise is over a threshold, it induces some regular structure that will be synchronized with the behaviour of neighbour elements. To obtain such a system with a threshold one needs one more noise source. Dr. Zaikin has analyzed further examples of such doubly stochastic effects and developed a concept of these new phenomena. These theoretical findings are important, because such processes can play a crucial role in neurophysics, technical communication devices and living sciences.
My thesis is concerned with several new noise-induced phenomena in excitable neural models, especially those with FitzHugh-Nagumo dynamics. In these effects the fluctuations intrinsically present in any complex neural network play a constructive role and improve functionality. I report the occurrence of Vibrational Resonance in excitable systems. Both in an excitable electronic circuit and in the FitzHugh-Nagumo model, I show that an optimal amplitude of high-frequency driving enhances the response of an excitable system to a low-frequency signal. Additionally, the influence of additive noise and the interplay between Stochastic and Vibrational Resonance is analyzed. Further, I study systems which combine both oscillatory and excitable properties, and hence intrinsically possess two internal frequencies. I show that in such a system the effect of Stochastic Resonance can be amplified by an additional high-frequency signal which is in resonance with the oscillatory frequency. This amplification needs much lower noise intensities than for conventional Stochastic Resonance in excitable systems. I study frequency selectivity in noise-induced subthreshold signal processing in a system with many noise-supported stochastic attractors. I show that the response of the coupled elements at different noise levels can be significantly enhanced or reduced by forcing some elements into resonance with these new frequencies which correspond to appropriate phase-relations. A noise-induced phase transition to excitability is reported in oscillatory media with FitzHugh-Nagumo dynamics. This transition takes place via noise-induced stabilization of a deterministically unstable fixed point of the local dynamics, while the overall phase-space structure of the system is maintained. The joint action of coupling and noise leads to a different type of phase transition and results in a stabilization of the system. The resulting noise-induced regime is shown to display properties characteristic of excitable media, such as Stochastic Resonance and wave propagation. This effect thus allows the transmission of signals through an otherwise globally oscillating medium. In particular, these theoretical findings suggest a possible mechanism for suppressing undesirable global oscillations in neural networks (which are usually characteristic of abnormal medical conditions such as Parkinson′s disease or epilepsy), using the action of noise to restore excitability, which is the normal state of neuronal ensembles.
Earth's climate varies continuously across space and time, but humankind has witnessed only a small snapshot of its entire history, and instrumentally documented it for a mere 200 years. Our knowledge of past climate changes is therefore almost exclusively based on indirect proxy data, i.e. on indicators which are sensitive to changes in climatic variables and stored in environmental archives. Extracting the data from these archives allows retrieval of the information from earlier times. Obtaining accurate proxy information is a key means to test model predictions of the past climate, and only after such validation can the models be used to reliably forecast future changes in our warming world. The polar ice sheets of Greenland and Antarctica are one major climate archive, which record information about local air temperatures by means of the isotopic composition of the water molecules embedded in the ice. However, this temperature proxy is, as any indirect climate data, not a perfect recorder of past climatic variations. Apart from local air temperatures, a multitude of other processes affect the mean and variability of the isotopic data, which hinders their direct interpretation in terms of climate variations. This applies especially to regions with little annual accumulation of snow, such as the Antarctic Plateau. While these areas in principle allow for the extraction of isotope records reaching far back in time, a strong corruption of the temperature signal originally encoded in the isotopic data of the snow is expected. This dissertation uses observational isotope data from Antarctica, focussing especially on the East Antarctic low-accumulation area around the Kohnen Station ice-core drilling site, together with statistical and physical methods, to improve our understanding of the spatial and temporal isotope variability across different scales, and thus to enhance the applicability of the proxy for estimating past temperature variability. The presented results lead to a quantitative explanation of the local-scale (1–500 m) spatial variability in the form of a statistical noise model, and reveal the main source of the temporal variability to be the mixture of a climatic seasonal cycle in temperature and the effect of diffusional smoothing acting on temporally uncorrelated noise. These findings put significant limits on the representativity of single isotope records in terms of local air temperature, and impact the interpretation of apparent cyclicalities in the records. Furthermore, to extend the analyses to larger scales, the timescale-dependency of observed Holocene isotope variability is studied. This offers a deeper understanding of the nature of the variations, and is crucial for unravelling the embedded true temperature variability over a wide range of timescales.
Nonlinear multistable systems under the influence of noise exhibit a plethora of interesting dynamical properties. A medium noise level causes hopping between the metastable states. This attractorhopping process is characterized through laminar motion in the vicinity of the attractors and erratic motion taking place on chaotic saddles, which are embedded in the fractal basin boundary. This leads to noise-induced chaos. The investigation of the dissipative standard map showed the phenomenon of preference of attractors through the noise. It means, that some attractors get a larger probability of occurrence than in the noisefree system. For a certain noise level this prefernce achieves a maximum. Other attractors are occur less often. For sufficiently high noise they are completely extinguished. The complexity of the hopping process is examined for a model of two coupled logistic maps employing symbolic dynamics. With the variation of a parameter the topological entropy, which is used together with the Shannon entropy as a measure of complexity, rises sharply at a certain value. This increase is explained by a novel saddle merging bifurcation, which is mediated by a snapback repellor. Scaling laws of the average time spend on one of the formerly disconnected parts and of the fractal dimension of the connected saddle describe this bifurcation in more detail. If a chaotic saddle is embedded in the open neighborhood of the basin of attraction of a metastable state, the required escape energy is lowered. This enhancement of noise-induced escape is demonstrated for the Ikeda map, which models a laser system with time-delayed feedback. The result is gained using the theory of quasipotentials. This effect, as well as the two scaling laws for the saddle merging bifurcation, are of experimental relevance.