Neural synaptic properties and conductance kinetics jointly influence how neurons process correlated input

Neurons transmit information through spikes. Given the prevalence of correlation among neural spike trains experimentally observed in different brain areas, it is of interest to study how neurons compute correlated input. Yet how it depends on the synaptic properties and conductance kinetics is very little known. Through simulation of leaky integrate-and-fire (LIF) neurons, we studied the effects of synaptic decay times, level of input activities and conductance fluctuation on the output correlation of different time scales for neurons receiving correlated excitatory input. We showed that the ratio of long-term correlation to short-term correlation (synchrony) increases with excitatory synaptic decay time due to the combined effects of jittered spike time and burst firing. In particular, it is possible for neurons with small excitatory synaptic decay time in high conductance state to give extra precisely timed synchronous spikes without exhibiting correlation of longer timescales in response to correlated input. In addition, we showed that burst firing greatly enhances output correlation but not synchrony, leading to an increase in correlation when conductance fluctuation is ignored.