Detection of pulses in a colored noise setting

Cortical neurons are exposed to a considerable amount of synaptic background activity, which increases the neurons' conductance and which leads to a fluctuating membrane potential. Here we investigate how the presence and the properties of this background noise influence the ability of a neuron to detect transient inputs, a task that is important for coincidence detection as well as for the detection of synchronous spiking events in a neural system. Using a leaky integrate-and-fire neuron as well as a biologically more realistic Hodgkin-Huxley type point neuron we find that noise enhances the detection of subthreshold input pulses and that the phenomenon of stochastic resonance occurs. When the noise is colored, pulse detection becomes more robust, because the number of false positive events decreases with increasing temporal correlation while the number of correctly detected events is almost unaffected. Therefore, the optimal variance of the noise also changes with the degree of temporal correlations of the background activity. For the integrate-and-fire model these effects can be described using an ansatz by Brunel and Sergi [J. Theor. Biol. 195, 87 (1998)]. Numerical simulations show that the leaky integrate-and-fire model and the Hodgkin-Huxley type point neuron behave qualitatively similarly.