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Computational Neurophysics

Computational Neurophysics teamTeam members: Paulina Dabrowska, Younes Bouhadjar, Maren Maus, Anno Kurth, Markus Diesmann, Stefan Dasbach, Karolina Korvasova, Johanna Senk, Tom Tetzlaff and Renan Shimoura
Copyright: INM-6, Forschungszentrum Jülich

The focus of this group are bottom-up approaches in order to integrate physiological and anatomical data (CoCoMac) into models, in particular model development, theory of neuronal networks, and correlation dynamics (NEST)

The cortical neuronal network is among the most complex structures found in nature. The functional role of its dynamics exhibited on many spatio-temporal scales is presently not understood. Furthermore, in contrast to other systems, the structure of the cortex is in fact not static but undergoes a continuous activity dependent reorganization. Such reorganization is influenced and controlled by the activity in the network.

The group of Markus Diesmann in the Laboratory for Computational and Systems Neuroscience studies the dynamics of recurrent neural networks, integrating the structural knowledge from neural anatomy and the functional consequences of the interplay of spike synchronization and plasticity in biologically realistic models of the cortical network. These investigations depend on large-scale simulations requiring non-standard algorithms and high-performance parallel computing. Therefore, the laboratory is also concerned with the creation of appropriate simulation technology (NEST).

Multi-scale network modeling