Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds
SourceSeminar Date of Publication:Oct 13,2022
10/13 2022 Seminar
- Title Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds
- Speaker Louis Tao (陶乐天) 教授（北京大学）
- Date 2022年10月13日 10:30-11:30
- Venue 腾讯会议号：406 699 187 密码：800709
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 nonlinearity, 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. 报告人简介：Prof. Louis Tao is working at the Center for Bioinformatics of Peking University. He was transplanted from Taipei to New York at an early age and had dreams of becoming an astrophysicist. Later on, after two degrees and two postdocs in Physics, he found computational neuroscience to be his true calling. Most recently he has worked on modeling primary visual cortex, theoretical aspects of neuronal population dynamics, information transfer and processing in neural circuits, neuromorphic computations, and live, optical imaging of C. elegans behavior and its underlying neural circuits.