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Slow fluctuations in recurrent networks of spiking neurons

Stefan Wieland, Davide Bernardi, Tilo Schwalger, and Benjamin Lindner
Phys. Rev. E 92, 040901(R) – Published 5 October 2015

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.

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  • Received 24 July 2015

DOI:https://doi.org/10.1103/PhysRevE.92.040901

©2015 American Physical Society

Authors & Affiliations

Stefan Wieland1,2, Davide Bernardi1,2, Tilo Schwalger3, and Benjamin Lindner1,2

  • 1Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany
  • 2Physics Department of Humboldt University Berlin, Newtonstrasse 15, 12489 Berlin, Germany
  • 3School of Computer and Communication Sciences and School of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Station 15, 1015 Lausanne EPFL, Switzerland

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Vol. 92, Iss. 4 — October 2015

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