Detecting long-range correlations in time series of neuronal discharges

Abstract

We have studied the discharge dynamics of dorsal horn neurons (DHN) by applying the detrended fluctuation analysis (DFA) and the wavelet transform (WT) technique. We have adopted that discharge dynamics is manifested by the random time series of the interspike intervals (ISI), that is, by intervals between two consecutive neuronal electrical activities. In all cases studied, we found two different power-law type behaviors across ISI enumeration scale, that are separated by a crossover region. Our results reveal that complex neuronal dynamics may change in the presence of external stimulation, which is manifested by changing the noise characteristics that appear before the crossover region (the noise after the crossover region is of the 1/f type).