Quantifying time-varying multiunit neural activity using entropy-based measures

Modern microelectrode arrays make it possible to simultaneously record population neural activity. However, methods to analyze multiunit activity (MUA), which reflects the aggregate spiking activity of a population of neurons, have remained underdeveloped in comparison to those used for studying single unit activity (SUA). In scenarios where SUA is hard to record and maintain or is not representative of brains response, MUA is informative in deciphering the brains complex time-varying response to stimuli or to clinical insults. Here, we present two quantitative methods of analysis of the time-varying dynamics of MUA without spike detection. These methods are based on the multiresolution discrete wavelet transform (DWT) of an envelope of MUA (eMUA) followed by information theoretic measures: multiresolution entropy (MRE) and the multiresolution KullbackLeibler distance (MRKLD). We test the proposed quantifiers on both simulated and experimental MUA recorded from rodent cortex in an experimental model of global hypoxicischemic brain injury. First, our results validate the use of the eMUA as an alternative to detecting and analyzing transient and complex spike activity. Second, the MRE and MRKLD are shown to respond to dynamic changes due to the brains response to global injury and to identify the transient changes in the MUA.