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Shape analysis of gamma rhythm supports a superlinear inhibitory regime in an inhibitionstabilized network

Krishnakumaran, R and Raees, M and Ray, S (2022) Shape analysis of gamma rhythm supports a superlinear inhibitory regime in an inhibitionstabilized network. In: PLoS Computational Biology, 18 (2).

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Official URL: https://doi.org/10.1371/journal.pcbi.1009886


Visual inspection of stimulus-induced gamma oscillations (30-70 Hz) often reveals a nonsinusoidal shape. Such distortions are a hallmark of non-linear systems and are also observed in mean-field models of gamma oscillations. A thorough characterization of the shape of the gamma cycle can therefore provide additional constraints on the operating regime of such models. However, the gamma waveform has not been quantitatively characterized, partially because the first harmonic of gamma, which arises because of the nonsinusoidal nature of the signal, is typically weak and gets masked due to a broadband increase in power related to spiking. To address this, we recorded local field potential (LFP) from the primary visual cortex (V1) of two awake female macaques while presenting fullfield gratings or iso-luminant chromatic hues that produced huge gamma oscillations with prominent peaks at harmonic frequencies in the power spectra. We found that gamma and its first harmonic always maintained a specific phase relationship, resulting in a distinctive shape with a sharp trough and a shallow peak. Interestingly, a Wilson-Cowan (WC) model operating in an inhibition stabilized mode could replicate this shape, but only when the inhibitory population operated in the super-linear regime, as predicted recently. However, another recently developed model of gamma that operates in a linear regime driven by stochastic noise failed to produce salient harmonics or the observed shape. Our results impose additional constraints on models that generate gamma oscillations and their operating regimes. © 2022 Krishnakumaran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Item Type: Journal Article
Publication: PLoS Computational Biology
Publisher: Public Library of Science
Additional Information: The copyright for this article belongs to Public Library of Science
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Division of Physical & Mathematical Sciences > Mathematics
Date Deposited: 18 Mar 2022 11:55
Last Modified: 18 Mar 2022 11:55
URI: http://eprints.iisc.ac.in/id/eprint/71582

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