@article{f2aa3bd056af4492b1e51ed6b35cfce9,
title = "Modeling the electrical field created by mass neural activity",
abstract = "Gamma oscillations of large scale electrical activity are used in electrophysiological studies as markers for neural activity and functional processes in the cortex, yet the nature of this mass neural phenomenon and its relation to the evoked response potentials (ERP) are still not well understood. Many studies associated the gamma oscillations with oscillators around the 40 Hz frequency, yet recent studies have shown that gamma frequencies may be part of a broadband phenomenon ranging from 30 Hz up to 250 Hz. In this study we have examined the possibility that a simple model, based on available neurophysiological parameters, involving an increase in asynchronous (Poisson distributed) neural firing may be sufficient to generate the observed gamma power increases. Our simulation shows a roughly linear increase in gamma power as a function of the aggregated firing rate of the neural population, while the influence of the synchronization level within the neurons on the gamma power is limited. Our model supports the viewpoint that the broadband gamma response is mainly driven by the summed, asynchronous, activity of the neural population. We show that the time frequency spectrogram of the stimulus response can be reconstructed by combining two different phenomena-the broadband gamma power increase due to local processing and the more spatially distributed event related desynchronization (ERD). Our model thus raises the possibility that the broadband gamma response is closely linked to the aggregate population firing rate of the recorded neurons.",
keywords = "ECoG, ERD, Gamma oscillations, Simulation",
author = "Eran Privman and Rafael Malach and Yehezkel Yeshurun",
note = "Funding Information: This work was supported by ISF , Bikura , The Hellen and Martin Kimmel Award for Innovative Investigation and Mark Scher{\textquoteright}s estate grants to RM.",
year = "2013",
month = apr,
doi = "10.1016/j.neunet.2013.01.004",
language = "אנגלית",
volume = "40",
pages = "44--51",
journal = "Neural Networks",
issn = "0893-6080",
publisher = "Elsevier Ltd.",
}