• Open Access

Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

Hongjie Bi, Marco Segneri, Matteo di Volo, and Alessandro Torcini
Phys. Rev. Research 2, 013042 – Published 13 January 2020

Abstract

Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum γ oscillations have received particular attention due to their ubiquitous nature and their correlation with higher brain functions. Recently, it has been reported that γ oscillations in the hippocampus of behaving rodents are segregated in two distinct frequency bands: slow and fast. These two γ rhythms correspond to different states of the network, but their origin has been not yet clarified. Here we show theoretically and numerically that a single inhibitory population can give rise to coexisting slow and fast γ rhythms corresponding to collective oscillations of a balanced spiking network. The slow and fast γ rhythms are generated via two different mechanisms: the fast one being driven by the coordinated tonic neural firing and the slow one by endogenous fluctuations due to irregular neural activity. We show that almost instantaneous stimulations can switch the collective γ oscillations from slow to fast and vice versa. Furthermore, to draw a connection with the experimental observations, we consider the modulation of the γ rhythms induced by a slower (θ) rhythm driving the network dynamics. In this context, depending on the strength of the forcing and the noise amplitude, we observe phase-amplitude and phase-phase coupling between the fast and slow γ oscillations and the θ forcing. Phase-phase coupling reveals on average different θ-phase preferences for the two coexisting γ rhythms joined to a wide cycle-to-cycle variability.

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  • Received 28 December 2018
  • Revised 29 June 2019

DOI:https://doi.org/10.1103/PhysRevResearch.2.013042

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Statistical PhysicsNetworksInterdisciplinary PhysicsNonlinear Dynamics

Authors & Affiliations

Hongjie Bi1,*, Marco Segneri1,†, Matteo di Volo1,2,‡, and Alessandro Torcini1,§

  • 1Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR No. 8089, 95302 Cergy-Pontoise Cedex, France
  • 2Unité de Neuroscience, Information et Complexité, CNRS FRE No. 3693, 1 Avenue de la Terrasse, 91198 Gif sur Yvette, France

  • *hongjie.bi@u-cergy.fr
  • marco.segneri@u-cergy.fr
  • matteo.divolo@unic.cnrs-gif.fr
  • §alessandro.torcini@u-cergy.fr

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Vol. 2, Iss. 1 — January - March 2020

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