  We find that sensory noise delivered together with a weak periodic signal not
only enhances nonlinear response of neuronal networks, but also improves the
synchronization of the response to the signal. We reveal this phenomenon in
neuronal networks that are in a dynamical state near a saddle-node bifurcation
corresponding to appearance of sustained network oscillations. In this state,
even a weak periodic signal can evoke sharp nonlinear oscillations of neuronal
activity. These sharp network oscillations have a deterministic form and
amplitude determined by nonlinear dynamical equations. The signal-to-noise
ratio reaches a maximum at an optimum level of sensory noise, manifesting
stochastic resonance (SR) at the population level. We demonstrate SR by use of
simulations and numerical integration of rate equations in a cortical model
with stochastic neurons. Using this model, we mimic the experiments of Gluckman
et al. [B. J. Gluckman et al, Phys. Rev. Lett., v. 77, 4098 (1996)] that have
given evidence of SR in mammalian brain. We also study neuronal networks in
which neurons are grouped in modules and every module works in the regime of
SR. We find that even a few modules can strongly enhance the reliability of
signal detection in comparison with the case when a modular organization is
absent.
