Noise Induces Spontaneous Synchronous Aperiodic Activity in EI Neural Networks

Abstract

We analyze the effect of noise on spontaneous activity of a excitatory-inhibitory neural network model. Analytically different regimes can be distinguished depending on the network parameters. In one of the regimes noise induces synchronous aperiodic oscillatory activity in the isolated network (regime B). The Coherent Stochastic Resonance phenomena occur. Activity is highly spatially correlated (synchrony), it’s oscillatory on short time scales and it decorrelates in time on long time scale (aperiodic). At zero noise the oscillatory activity vanishes in this regime. Changing parameters (for example increasing the excitatory-to-excitatory connection strength) we get spontaneous synchronous and periodic activity, even without noise (regime C). The model is in agreement with the measurements of spontaneous activity of two-dimensional cortical cell neural networks placed on multi-electrode arrays performed by Segev et.al [2].