Regensburg 2016 – wissenschaftliches Programm
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BP: Fachverband Biologische Physik
BP 22: Posters - Neurosciences
BP 22.6: Poster
Montag, 7. März 2016, 17:30–19:30, Poster C
The effect of noise on the transition to chaos in random neural networks — •Sven Goedeke1,4, Jannis Schuecker1,4, Markus Diesmann1,2,3, and Moritz Helias1,3 — 1Inst of Neurosci and Medicine (INM-6) and Inst for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre — 2Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University — 3Department of Physics, Faculty 1, RWTH Aachen University — 4These authors contributed equally
Networks of randomly coupled rate neurons display a transition to chaos at a critical coupling strength (Sompolinsky et al. 1988, PRL). The dynamics close to the transition -- at the edge of chaos -- provides a powerful substrate for computations. Here, we investigate the effect of additive white noise, representing intrinsic stochasticity or external inputs, on the transition. We develop the dynamical mean-field theory yielding the autocorrelation function. Solving the eigenvalue problem for the maximum Lyapunov exponent allows us to analytically determine the transition from non-chaotic to chaotic activity. Increasing the noise amplitude shifts the transition to larger coupling strengths, i.e., chaos is suppressed. The decay time of the autocorrelation function does not diverge at the transition, but peaks slightly above the critical coupling strength. Partly supported by Helmholtz association: VH-NG-1028 and SMHB; EU Grant 604102 (HBP).