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We find that sensory noise delivered together with a weak periodic signal notonly enhances nonlinear response of neuronal networks, but also improves thesynchronization of the response to the signal. We reveal this phenomenon inneuronal networks that are in a dynamical state near a saddle-node bifurcationcorresponding to appearance of sustained network oscillations. In this state,even a weak periodic signal can evoke sharp nonlinear oscillations of neuronalactivity. These sharp network oscillations have a deterministic form andamplitude determined by nonlinear dynamical equations. The signal-to-noiseratio reaches a maximum at an optimum level of sensory noise, manifestingstochastic resonance (SR) at the population level. We demonstrate SR by use ofsimulations and numerical integration of rate equations in a cortical modelwith stochastic neurons. Using this model, we mimic the experiments of Gluckmanet al. [B. J. Gluckman et al, Phys. Rev. Lett., v. 77, 4098 (1996)] that havegiven evidence of SR in mammalian brain. We also study neuronal networks inwhich neurons are grouped in modules and every module works in the regime ofSR. We find that even a few modules can strongly enhance the reliability ofsignal detection in comparison with the case when a modular organization isabsent.

Noise-enhanced nonlinear response and the role of modular structure for signal detection in neuronal networks