Stochastiic Variability

Simulation of Neuronal Signal Processing - 2020-03-05

Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

Mar 5, 12:00 - 13:00

B9 L2 R2322

Neuronal ensembles Stochastiic Variability neuroscience

In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.