Slow fluctuations in recurrent networks of spiking neurons

Networks 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.


Published in:
Physical Review E, 92, 4
Year:
2015
Publisher:
College Pk, Amer Physical Soc
ISSN:
1550-2376
Keywords:
Laboratories:




 Record created 2015-10-14, last modified 2018-12-03

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