Ongoing Hippocampal Neuronal Activity in Human: Is it Noise or Correlated Fractal Process?

Abstract

The patterns of background or ongoing in vivo activity, even in the absence of any external stimulus, are quite irregular showing no clear structure or repetitiveness in the neuronal firing sequences. Consequently, the ongoing firing pattern of a neuron is mostly considered as a neuronal noise which is traditionally modeled as a stochastic Point process, i.e., renewal process which is devoid of any correlation between successive inter-spike-interval (ISI). But a recently emerging alternative view is that the ongoing activity may possess sptaio-temporally coherent patterns, a feature of fractal process with long-range correlation. Here, we investigated the nature of irregular fluctuations of ongoing neuronal firing pattern of neurons located in human hippocampus by the following methods: (i) detrended fluctuation analysis (DFA) , (ii) multiscale entropy (MSE) analysis, and (iii) convergence of the statistical moment analysis (CMA). Neuronal activity was recorded in the absence of any explicit cognitive task while the subjects were awake. Both the DFA and MSE analysis clearly show that the ongoing firing patterns are not well described by a renewal process, rather they show long-range power-law correlations, representing ongoing memory effects, which possibly arises from a fractal process. Further, these neurons showed slow convergence of statistical moments. Such long-range correlations are also corroborated by statistical control sequences. Neurons which exhibit long-range correlations also exhibit statistically nonsignificant correlations with other neighboring neurons. The presence of long-range correlations is a characteristic of fractal-like dynamics, representing memory or history in the firing patterns. We propose that this type of spatio-temporal correlations may be used to optimize information transfer and storage at hippocampal synapses. The presence of correlation in the ongoing pattern also suggests the influence of pre-stimulus sequence on shaping the post-stimulus responses. Further, these findings call for the modification of the existing neural modeling approaches.