What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance.

By Nicolas J.-B. Brunel, Xiao-Jing Wang. Journal of Neurophysiology, 2003

882 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/92092e12daea35db633ffd953356f97227b0ff36

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Citations in the corpus

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, background): “gence of fast oscillations in local field potentials [132].”
Gamma and beta bursts underlie working memory (2016, neutral): “Early network models (Brunel and Wang, 2003) with the same pyramidal-interneuron interaction as in the tested model (Figure 1C) suggest that both gamma frequency and spike rates are co-modulated by excitation.”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, methodology): “Interestingly, it can be shown that the oscillation frequency produced by the E-I loop is generally slower than that by the I-I loop (127, 342).”
The ups and downs of β oscillations in sensorimotor cortex. (2013, neutral): “…oscillation frequency may be related to the extent of neuronal networks (Kopell et al., 2000; Miller, 2007; von Stein and Sarnthein, 2000) or the underlying excitation-inhibition balance (Brunel and Wang, 2003; Buzsáki, 2006; Jensen et al., 2005; Ray and Maunsell, 2010; Whittington et al., 2000).”
Mechanisms of Gamma Oscillations (2012, background): “In most E-I models, there is no need for I-I connections (Wilson & Cowan 1972, Whittington et al. 2000, Borgers & Kopell 2003, Brunel & Wang 2003, Geisler et al. 2005).”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, methodology): “Basic neural mass or mean-field models capture the changes in the mean firing rate (Brunel and Wang, 2003), while more advanced models use parameter dispersion in the neurons and therefore have a richer dynamical repertoire (Stefanescu and Jirsa, 2008).”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, methodology): “Using a combination of analytical techniques (such as themean-ﬁeldapproach)andcomputersimulations,they found that the network behavior is characterized by stable spiking patterns.”
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models (2015, methodology): “However, LIF networks can generate a variety of different dynamic network states when the frequency of external inputs is varied [14,15,18].”
On the nature of seizure dynamics. (2014, background): “…e.g. Nernst equation of electrochemical equilibrium, conservation of mass and/or energy, or mean fields (Deco et al., 2008), leading to neural population or ‘mass’ models (Wilson and Cowan, 1972; Nunez, 1974; Freeman, 1975; Jansen and Rit, 1995; Brunel and Wang, 2003; Stefanescu and Jirsa, 2008).”
A review for dynamics in neuron and neuronal network (2017, background): “The normal function of neural networks depends on a delicate balance between excitatory and inhibitory synaptic inputs [46,47].”
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, neutral): “…of experimental and theoretical studies showing that neural activity at the population level usually exhibits oscillations with a moderate level of synchrony, in particular in the gamma frequency range (30–80 Hz) (Bartos et al., 2007; Borgers and Kopell, 2003; Brunel, 2000; Brunel and Wang, 2003).”