direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

Nonlinear Dynamics and Control: empirical networks and neurodynamics


Coordinator: Dr. habil. Philipp Hövel

Associated with the Bernstein Center for Computational Neuroscience Berlin (BCCN Berlin)

The research area is located in the field of nonlinear dynamics and control with strong connections to neuroscience and excitable system. Special attention is given to the phenomenon of synchronization, effects of time delay, network dynamics, stochastic dynamical systems as well as pattern formation and nonlinear excitation waves in neural systems.

See also press release by BCCN Berlin (in German) and flyer of BCCN Berlin

Neurodynamics and empirical networks


We analyze empirial networks, for instance, of livestock trade or fMRI measurements of the human brain. We use structures extracted from these data in our numerical simulations of nonlinear models for the investigation of dynamical behavior (spread of disease and neuronal dynamics, respectively). A speacial focus is put on temporal networks with time-dependent couplings. We study the controllability of time-varying networks, develop novel control schemes and test these on empirical networks. Our methods include measures from network science, bifurcation analysis and control theory. See also flyer of the junior research group Hövel (PDF, 159,8 KB)


Project B10 (SFB910): Control of networks with time-varying topologies and applications to epidemiology

Many networks exhibit time-dependent topologies with edges existing for some time or weights subject to temporal fluctuations. This is particularly important, if the evolution of the network topology acts on a timescale similar to the local node dynamics, and forms profound challenges for the control of coupled elements. Our objective is to develop a framework for the investigation of the dynamics on temporal networks. We address (i) the controllability of networks, (ii) apply novel control designs to time-dependent network topologies, and (iii) test our findings on high-resolution datasets, e.g. animal trade, with important applications in epidemiology.

See also list of projects of SFB 910.

Project A13: Dynamics of inhomogeneous neural systems with nonlocal coupling

We investigate the cooperative dynamics of nonlocally coupled neural populations. The individual systems display oscillatory local dynamics, e.g., above a Hopf bifurcation associated with excitability type II. Inhomogeneity of the local elements is introduced in the network via a distribution of system's parameters. Varying the network parameters - such as coupling radius and strength - and in dependence upon the variability of the system's parameters, we analyze spatio-temporal dynamics of coupled systems. Coherent solutions, their stability, and mechanisms of the transition from coherence to incoherence are investigated. Especially, we discuss the occurrence of chimera states that exhibit spatial coexistence of regular synchronized and irregular spatially incoherent regions. This will establish the universality of the coherence-incoherence bifurcation and contributes to a better understanding of synchronization in neural systems.

See also extended project description and list of projects (BCCN Berlin)

Project B7: Large-scale neural model for functional networks of the human cortex

We will address resting brain fluctuations in fMRI data combining experimental and theoretical approach. Based on empirically derived large-scale functional networks of the human cortex we will use numerical simulations to test hypothesis that (i) indirect connections, (ii) interregional distance, and (iii) collective effects governed by network properties of the cortex play significant a role in generation of the resting state fluctuations.

See also extended project description and list of projects (BCCN Berlin)


Zusatzinformationen / Extras