AUTHOR=Parto Dezfouli Mohsen , Zarei Mohammad , Jahed Mehran , Daliri Mohammad Reza 

TITLE=Stimulus-Specific Adaptation Decreases the Coupling of Spikes to LFP Phase

JOURNAL=Frontiers in Neural Circuits

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2019.00044

DOI=10.3389/fncir.2019.00044

ISSN=1662-5110

ABSTRACT=Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of stimulus-specific adaptation on the spike-LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms were evaluated. Here, we employed phase locking value (PLV) method for calculating the SPC. Our data shows a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. This highlights the crucial role of LFP phase and its relation to spikes in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.