Stochastic resonance using noise generated by a neural network

We study the phenomenon of stochastic resonance when the noise is generated by a network of integrate-and-fire neurons. A network with two populations of neurons (one excitatory and one inhibitory) and a dispersion of intrinsic firing rates can display a great amount of variability and its output can be used to improve the signal-to-noise ratio of another neuron that is receiving a subthreshold signal. We compare the performance of this system to that of a system using white noise with different distributions. We find network parameters for which the performance is similar to that of a system receiving white noise. In other cases, when the network output displays an oscillatory component, the signal-to-noise ratio has several peaks, We also analyze the relation between the synchrony of the network and the signal-to-noise ratio.