Realistic alpha oscillations and transient responses in a cortical microcircuit model

Abstract

Neural-mass modeling of neural population data (EEG, ECoG, or LFPs) has shown promise both in elucidating the neural processes underlying cortical rhythms and changes in brain state, as well as offering a framework for testing the interplay between these rhythms and information processing. Models of cortical alpha rhythms (8 - 12 Hz) and their impact in visual sensory processing have been at the forefront of this effort, with the Jansen-Rit being one of the more popular models in this domain. The Jansen-Rit model, however, fails in reproducing key physiological observations including the level of inputs that cortical neurons receive and their responses to visual transients. To address these issues we generated a neural mass model that complies better with synaptic mediated dynamics, intrinsic alpha behavior, and produces realistic responses. The model is robust to many changes in parameter values but critically depends on the ratio of excitation to inhibition, producing response transients whose features are dependent on this ratio and alpha phase and power. The model is sufficiently flexible so as to be able to easily replicate the range of low frequency oscillations observed in different studies. Consistent with experimental observations, we find phase-dependent response dynamics to both visual and electrical stimulation using this model. The model suggests that stimulation facilitates alpha at particular phases and suppresses it in others due to a phase dependent lag in inhibitory responses. Hence, the model generates insight into the physiological parameters responsible for intrinsic oscillations and testable hypotheses regarding the interactions between visual and electrical stimulation on those oscillations. Author summaryWhile there is increasing evidence of the fundamental role brain states play in shaping information processing in the cerebral cortex, a mechanistic understanding of how those brain states are manifested and alter the signals underlying sensory processing and decision making has proved challenging. To address this issue we have modeled a well established signature of inattention in visual cortex: synchronized low frequency (8 - 12 Hz) oscillations. To allow for inferences regarding the local generation of these rhythms within a cortical microcircuit we used a neural mass model approach that incorporates physiologically realistic interactions between 3 populations of neurons. Our model is able to explain a variety of experimental observations that previous neural mass models have not, including spontaneous rhythms in the absence of input, the faithful transmission of strong input transients, a range of oscillation frequencies, and phase dependent visual responses. The model is robust to a range of parameters, but critically depends on the balance between local excitation and inhibition.