Wieland, StefanBernardi, DavideSchwalger, TiloLindner, Benjamin2015-10-142015-10-142015-10-14201510.1103/PhysRevE.92.040901https://infoscience.epfl.ch/handle/20.500.14299/119810WOS:000362445200002Networks of fast nonlinear elements may display slowfluctuations if interactions are strong. We find a transition in the long-term variability of a sparse recurrent network of perfect integrate-and-fire neurons at which the Fano factor switches from zero to infinity and the correlation time is minimized. This corresponds to a bifurcation in a linear map arising from the self-consistency of temporal input and output statistics. More realistic neural dynamics with a leak current and refractory period lead to smoothed transitions and modified critical couplings that can be theoretically predicted.neural networksneural dynamicscolored noisespiking neurontext::journal::journal article::research article