Abstract

Gamma frequency oscillations (25–140 Hz), observed in the neural activities within many brain regions, have long been regarded as a physiological basis underlying many brain functions, such as memory and attention. Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. However, due to its high dimensionality and strong non-linearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics. Here we propose a suite of Markovian model reduction methods with varying levels of complexity and apply it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from nearly homogeneous firing to strong synchrony in the gamma band. The reduced models not only successfully reproduce gamma oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. Most remarkably, the invariant measure of the coarse-grained Markov process reveals a two-dimensional surface in state space upon which the gamma dynamics mainly resides. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. Because of the generality of the Markovian assumptions, our dimensional reduction methods offer a powerful toolbox for theoretical examinations of other complex cortical spatio-temporal behaviors observed in both neurophysiological experiments and numerical simulations.

Highlights

  • Modern experimental techniques have revealed a vast diversity of coherent spatiotemporal activity patterns in the brain, reflecting the many possible interactions between excitation and inhibition, between cellular and synaptic time-scales, and between local and long-range circuits

  • Interneuron network gamma (ING) proposes that the gamma oscillations can be produced by the fast interactions between inhibitory (I) neurons alone (Whittington et al, 2000)

  • interneuron network gamma (ING) does not require the existence of excitatory (E) neurons but studies showed that it loses its coherence in systems where neurons are heterogeneously driven (Wang and Buzsáki, 1996)

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Summary

Introduction

Modern experimental techniques have revealed a vast diversity of coherent spatiotemporal activity patterns in the brain, reflecting the many possible interactions between excitation and inhibition, between cellular and synaptic time-scales, and between local and long-range circuits. Prominent amongst these patterns are the rich repertoire of neuronal oscillations that can be stimulus driven or internally generated and are likely to be responsible for sensory perception and cognitive tasks (Fries, 2009; Tallon-Baudry, 2009). Much experimental evidence correlates gamma oscillations to behavior and enhanced sensory or cognitive performances. The disruption of gamma frequency synchronization is concomitant with multiple brain disorders (Bressler, 2003; Baar, 2013; McNally and McCarley, 2016; Krystal et al, 2017; Mably and Colgin, 2018)

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