Title: "Collective Stochastic Resonance".

Abstract:
Using a stochastic model of neural networks, we 
investigate analytically and numerically response 
of neural networks on weak time dependent stimuli 
in the presence of noise. We propose a new 
mechanism of stochastic resonance, so called 
"collective stochastic resonance". It occurs in 
neural networks which undergo a transition from a 
state with short-range spatio-temporal 
correlations between neurons to a state with 
network oscillations. We find that dynamical 
stochastic resonance emerges as the precursor of 
global oscillations. It increases the output 
signal-to-noise ratio and improves the 
recognition of weak sensory stimuli. We show that 
a modular organization of neural networks can 
improve the reliability of detection of weak 
signals.