  The mechanisms and impact of correlated, or synchronous, firing among pairs
and groups of neurons is under intense investigation throughout the nervous
system. A ubiquitous circuit feature that can give rise to such correlations
consists of overlapping, or common, inputs to pairs and populations of cells,
leading to common spike train responses. Here, we use computational tools to
study how the transfer of common input currents into common spike outputs is
modulated by the physiology of the recipient cells. We focus on a key
conductance - gA, for the A-type potassium current - which drives neurons
between "Type II" excitability (low gA), and "Type I" excitability (high gA).
Regardless of gA, cells transform common input fluctuations into a ten- dency
to spike nearly simultaneously. However, this process is more pronounced at low
gA values, as previously predicted by reduced "phase" models. Thus, for a given
level of common input, Type II neurons produce spikes that are relatively more
correlated over short time scales. Over long time scales, the trend reverses,
with Type II neurons producing relatively less correlated spike trains. This is
because these cells' increased tendency for simultaneous spiking is balanced by
opposing tendencies at larger time lags. We demonstrate a novel implication for
neural signal processing: downstream cells with long time constants are
selectively driven by Type I cell populations upstream, and those with short
time constants by Type II cell populations. Our results are established via
high-throughput numerical simulations, and explained via the cells' filtering
properties and nonlinear dynamics.
