State dependent complexity of local field potentials in the primary visual cortex

Abstract

The Local field potential (LFP) captures the combined electric activity of neurons in a region of brain tissue. Although widely used to study brain rhythms and neural circuits, the precise relationship between LFPs and spiking activity remains poorly understood, in particular, regarding cortical state. Using a symbolic representation based on Bandt-Pompe technique, this work bridges that gap by finding consistent relations between cortical state, as proxied by the coefficient of variation (CV) of spiking activity and information-theory quantifiers-Shannon entropy and statistical complexity—of LFP data from urethane-anesthetized rats and freely moving mice. Our findings demonstrate that LFP’s statistical complexity and Shannon entropy in the deep layers of the primary visual cortex (V1) vary consistently with spiking variability. We further explored these quantifiers across cortical layers in V1, showing that statistical complexity is sensitive to depth, peaking in mid-layer 5 as CV increases. Additionally, we show LFP’s statistical complexity varies across behavioral states of freely moving mice.