Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds.

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

SUBMITTER: Cai Y 

PROVIDER: S-EPMC8418102 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature