AUTHOR=Zarei Mohammad , Jahed Mehran , Daliri Mohammad Reza 

TITLE=Introducing a Comprehensive Framework to Measure Spike-LFP Coupling

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 12 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2018.00078

DOI=10.3389/fncom.2018.00078

ISSN=1662-5188

ABSTRACT=Measuring the coupling of single neuron’s spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC) and pairwise phase consistency (PPC). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To solve these problems the PPC measure has been introduced. However, there are some shortcomings associated to the PPC measure. PPC is unbiased only for very high spike rates while measuring spike-LFP phase coupling (SPC) in short trials or for low number of spikes is considered a critical phenomenon in many studies. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms for neurons with low spike rates. The results show that this framework significantly enhances the accuracy and provides a bias-free basis for small number of spikes for SPC as compared to the conventional methods such as PLV method. As such, it has the general ability to correct for the bias on number of spike rates.