Berlin 2012 – wissenschaftliches Programm
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BP: Fachverband Biologische Physik
BP 3: Statistical Physics of Biological Systems I (with DY)
BP 3.2: Vortrag
Montag, 26. März 2012, 09:45–10:00, MA 001
Complex activation patterns in a simple deterministic model of excitable neural networks — •Guadalupe C. Garcia1, Claus C. Hilgetag2, and Marc Thorsten Hütt1 — 1School of Engineering and Science, Jacobs University, Bremen, Germany — 2University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic model of excitation propagation to explore how network-wide activation patterns are shaped by neural network architecture.
The model consists of three discrete states for each node (susceptible S, excited E, refractory R), which are updated synchronously in discrete time steps according to a set of update rules allowing for signal propagation. In particular, an element returns to the susceptible state after r time steps. For small r, the network dynamics settle into an regular oscillatory behavior after a transient period. The set of nodes is thus partitioned into distinct groups of nodes, where two nodes are in the same group when they are simultaneously excited.
Two questions about this process are at the core of our investigation: (1) How does the dynamic partitioning into groups depend on network architecture (investigated by averaging the groupings over many different initial conditions)? (2) How does the length of the transient depend on network architecture? By exploring these deterministic excitation dynamics we aim at better understanding, which topological features facilitate self-sustained activity of neural networks.