  We study the dynamical properties of human communication through different
channels, i.e., short messages, phone calls, and emails, adopting techniques
from neuronal spike train analysis in order to characterize the temporal
fluctuations of successive inter-event times. We first measure the so-called
local variation (LV) of incoming and outgoing event sequences of users, and
find that these in- and out- LV values are positively correlated for short
messages, and uncorrelated for phone calls and emails. Second, we analyze the
response-time distribution after receiving a message to focus on the
input-output relationship in each of these channels. We find that the time
scales and amplitudes of response are different between the three channels. To
understand the impacts of the response-time distribution on the correlations
between the LV values, we develop a point process model whose activity rate is
modulated by incoming and outgoing events. Numerical simulations of the model
indicate that a quick response to incoming events and a refractory effect after
outgoing events are key factors to reproduce the positive LV correlations.
