Fig. 3.

Short-term depression reduces correlations between a pair of synaptic conductances. Autocovariance (A), cross-covariance (B), and spike count correlation (C) between two Poisson presynaptic spike trains. Vertical arrow in A represents a Dirac delta function. D–F: autocovariance function, cross-covariance function, and Pearson correlation (see methods) for the conductances produced by the presynaptic spike trains in A–C using a nondepressing, static synapse model. G–I: same, but for a synapse model that exhibits short-term depression with stochastic vesicle dynamics. All solid lines are for a presynaptic rate of ν_in = 15 Hz. In I, the correlation is shown at three additional presynaptic rates, ν_in =5, 10, and 20 Hz (dashed curves; rate increases with darkness of curves). All auto- and cross-covariance functions are normalized to have a maximum at one. Auto- and cross-covariance functions from Monte Carlo simulations are plotted along with those obtained from the analytical expressions in the methods, but the two are virtually indistinguishable. Synaptic depression drastically reduces correlations between the conductances, especially over longer time windows.