Slow fluctuations in recurrent networks of spiking neurons.
- Wieland S Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany.
- Bernardi D Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany.
- Schwalger T 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.
- Lindner B Bernstein Center for Computational Neuroscience Berlin, Philippstrasse 13, Haus 2, 10115 Berlin, Germany.
- 2015-11-14
Published in:
- Physical review. E, Statistical, nonlinear, and soft matter physics. - 2015
English
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.
- Language
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- English
- Open access status
- green
- Identifiers
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- DOI 10.1103/PhysRevE.92.040901
- PMID 26565154
- Persistent URL
- https://sonar.ch/global/documents/111843
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