Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons

The spike trains that transmit information between neurons are stochastic. We used the theory of random point processes and simulation methods to inve stigate the influence of temporal correlation of synaptic input current on firing statistics. The theory accounts for two sources for temporal correla tion: synchrony between spikes in presynaptic input trains and the unitary synaptic current time course. Simulations show that slow temporal correlati on of synaptic input leads to high variability in firing. In a leaky integr ate-and-fire neuron model with spike afterhyperpolarization the theory accu rately predicts the firing rate when the spike threshold is higher than two standard deviations of the membrane potential fluctuations. For lower thre sholds the spike afterhyperpolarization reduces the firing rate below the t heory's predicted level when the synaptic correlation decays rapidly. If th e synaptic correlation decays slower than the spike afterhyperpolarization, spike bursts can occur during single broad peaks of input fluctuations, in creasing the firing rate over the prediction. Spike bursts lead to a coeffi cient of variation for the interspike intervals that can exceed one, sugges ting an explanation of high coefficient of variation for interspike interva ls observed in vivo.