title: Slow fluctuations in recurrent networks of spiking neurons.
abstract: 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.
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany.|Physics Department of Humboldt University Berlin, Newtonstrasse 15, 12489 Berlin, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Wieland
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany.|Physics Department of Humboldt University Berlin, Newtonstrasse 15, 12489 Berlin, Germany.
authors.forename: Davide
authors.initials: D
authors.lastname: Bernardi
authors.affiliation: School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Station 15, 1015 Lausanne EPFL, Switzerland.
authors.forename: Tilo
authors.initials: T
authors.lastname: Schwalger
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany.|Physics Department of Humboldt University Berlin, Newtonstrasse 15, 12489 Berlin, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Lindner