Stochastic resonance and spike-timing precision in an ensemble of leaky integrate and fire neuron models

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Abstract
We analyze the transmission of sinelike periodic signals by an ensemble of leaky integrate-and-fire neuron models in the presence of additive noise. We observe that when the number of units in the ensemble is large enough, the point process formed by pooling the spike trains of all units is an inhomogeneous Poisson process. We obtain the intensity of this process, i.e., the instantaneous discharge rate of the ensemble, from the cycle histogram of the discharge of a single unit. This enables us to link measures of the regularity of the output discharge rate and the transmission of the periodic input, such as the signal to noise ratio and the input-output power norm and normalized power norm directly to the shape of the cycle histogram. Furthermore, we also show that firing precision in response to subthreshold stimulation is maximized at some intermediate noise value, and argue that in this regime the ensemble can reliably transmit fast periodic signals below the resolution of the individual units. Our analysis clarifies the conditions whereby noise enhances signal transmission and detection in ensembles.