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Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for general non-renewal processes to calculate the interval and count statistics of ensemble governed by a slow adaptation variable. For an ensemble of spike-frequency adapting neurons this results in the regularization of the population activity and an enhanced post-synaptic signal decoding.This result follows the theorem that we formally proofed that the ensemble activity represented by the superposition of many independent activity Fano factor converges to individual process CV2. We confirm our theoretical results in a population of cortical neurons. |
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