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

The A-current and Type I / Type II transition determine collective spiking from common input