Wieland S Bernardi D Schwalger T Lindner B 2015 Networks of fast nonlinear elements may display slow fluctuations 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. application/pdf https://sonar.ch/global/documents/111843 eng info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.92.040901 info:eu-repo/semantics/altIdentifier/pmid/26565154 info:eu-repo/semantics/openAccess Physical review. E, Statistical, nonlinear, and soft matter physics. - 2015 Slow fluctuations in recurrent networks of spiking neurons. http://purl.org/coar/resource_type/c_6501