Noise dynamically suppresses chaos in neural networks
Sven Goedeke, Jannis Schuecker, Moritz Helias · arXiv (Cornell University) · 2016
Noise is ubiquitous in neural systems due to intrinsic stochasticity or external drive. For deterministic dynamics, neural networks of randomly coupled units display a transition to chaos at a critical coupling strength. Here, we investigate the effect of additive white noise on the transition. We develop the dynamical mean-field theory yielding the statistics of the activity and the maximum Lyapunov exponent. A closed form expression determines the transition from the regular to the chaotic regime. Noise suppresses chaos by a dynamic mechanism, shifting the transition to significantly larger coupling strengths than predicted by local stability analysis. The decay time of the autocorrelation function does not diverge at the transition, but peaks slightly above the critical coupling strength.