  The local field potential (LFP) is as a measure of the combined activity of
neurons within a region of brain tissue. While biophysical modeling schemes for
LFP in cortical circuits are well established, there is a paramount lack of
understanding regarding the LFP properties along the states assumed in cortical
circuits over long periods. Here we use a symbolic information approach to
determine the statistical complexity based on Jensen disequilibrium measure and
Shannon entropy of LFP data recorded from the primary visual cortex (V1) of
urethane-anesthetized rats and freely moving mice. Using these information
quantifiers, we find consistent relations between LFP recordings and measures
of cortical states at the neuronal level. More specifically, we show that LFP's
statistical complexity is sensitive to cortical state (characterized by spiking
variability), as well as to cortical layer. In addition, we apply these
quantifiers to characterize behavioral states of freely moving mice, where we
find indirect relations between such states and spiking variability.
