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

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.

Exact neural mass model for synaptic-based working memory

“…The model has been developed to reproduce exactly the PLOS COMPUTATIONAL BIOLOGY macroscopic dynamics of heterogeneous QIF spiking neural networks with mesoscopic shortterm plasticity (m-STP), in the limit of an infinite number of neurons with Lorentzian distributed excitabilities. Even though the choice of the excitability distribution allows for an analytical derivation of the model, it does not limit the generality of the results [50,60]. As shown in sub-section Network dynamics versus neural mass evolution, the neural mass model reproduces well not only the QIF network dynamics with m-STP plasticity but, to a large extent, it reproduces also the dynamics of networks with plasticity implemented at a microscopic level (μ-STP).…”

“…This new generation of neural mass models has been recently used to describe the emergence of collective oscillations (COs) in fully coupled networks [53][54][55][56] as well as in balanced sparse networks [57]. Furthermore, it has been successfully employed to reveal the mechanisms at the basis of theta-nested gamma oscillations [58,59] and of the coexistence of slow and fast gamma oscillations [60]. However, to our knowledge, such models have not been yet generalized to spiking networks with plastic synapses.…”

“…This choice allows us to derive the neural mass model from the spiking QIF network analytically. However, the overall dynamics does not change substantially by considering other distributions for the neural excitabilities, like Gaussian and Binomial ones [50,60].…”

Patient-specific network connectivity combined with a next generation neural mass model to test clinical hypothesis of seizure propagation

“…Furthermore, it has been successfully employed to reveal the mechanisms at the basis of theta-nested gamma oscillations Segneri et al (2020); Ceni et al (2020) and the coexistence of slow and fast gamma oscillations Bi et al (2020). Finally it has been recently applied to modelling electrical synapses Montbrió and Pazó (2020) and working memory Taher et al (2020).…”

Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review

“…A number of other studies have employed mean-field reductions for populations of QIF neurons to elucidate how microscopic neural properties affect the macroscopic dynamics [228,229]. This includes insights into networks of heterogeneous QIF neurons with time delayed, all-to-all synaptic coupling [230,231], or two such networks [232].…”