Many large-scale network simulations ( Traub et al., 2005 Chariker and Young, 2015) and firing rate models ( Brunel and Hakim, 1999 Keeley et al., 2019) have been used to capture the wide range of the experimentally observed gamma band activity. The disruption of gamma frequency synchronization is also concomitant with multiple brain disorders ( Bressler, 2003 Baar, 2013 McNally and McCarley, 2016 Krystal et al., 2017 Mably and Colgin, 2018).
#Oacapture gamma control windows#
Numerical studies have demonstrated that coherent gamma oscillations between neuronal populations can provide temporal windows during which information transfer can be enhanced ( Womelsdorf et al., 2007). Experiments have implicated gamma oscillations during learning ( Bauer et al., 2007) and memory ( Pesaran et al., 2002). During cognitive tasks, the increases in gamma power in the visual pathway have been shown to correlate with attention ( Fries et al., 2001, 2008).
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For instance, gamma dynamics has been shown to sharpen orientation tuning in V1 and speed and direction tuning in MT ( Azouz and Gray, 2000, 2003 Frien et al., 2000 Liu and Newsome, 2006 Womelsdorf et al., 2012). Much experimental evidence correlates gamma oscillations to behavior and enhanced sensory or cognitive performances. In particular, gamma band oscillations (25–140 Hz), observed in multi-unit activity (MUA) and local field potential (LFP) measurements ( Ray and Maunsell, 2015), have been found in many brain regions (visual cortex ( Gray et al., 1989 Azouz and Gray, 2000 Logothetis et al., 2001 Henrie and Shapley, 2005), auditory cortex ( Brosch et al., 2002), somatosensory cortex ( Bauer et al., 2006), parietal cortex ( Pesaran et al., 2002 Buschman and Miller, 2007 Medendorp et al., 2007), frontal cortex ( Buschman and Miller, 2007 Gregoriou et al., 2009 Siegel et al., 2009 Sohal et al., 2009 Canolty et al., 2010 Sigurdsson et al., 2010 van Wingerden et al., 2010), hippocampus ( Bragin et al., 1995 Csicsvari et al., 2003 Colgin et al., 2009 Colgin, 2016), amygdala ( Popescu et al., 2009), and striatum ( Van Der Meer and Redish, 2009)). 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). 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. 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. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. 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.
#Oacapture gamma control full#
The reduced models not only successfully reproduce gamma oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. 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. However, due to its high dimensionality and strong non-linearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics.
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Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. 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.
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3Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Beijing, China.2School of Mathematical Sciences, Peking University, Beijing, China.1Department of Statistics, University of Chicago, Chicago, IL, United States.Yuhang Cai 1 †, Tianyi Wu 2,3 †, Louis Tao 3,4 * and Zhuo-Cheng Xiao 5 *