Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons

By Nicolas J.-B. Brunel. 2000

1,743 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “Many computational models of such spiking neurons have been proposed and analyzed (e.g., Amit & Brunel, 1997; Bohte, Kok, & La Poutre, 2002; Brea, Senn, & Pfister, 2013; Brette et al., 2007; Brunel, 2000; Deco & Rolls, 2005; Gerstner & Kistler, 2002; Gerstner & van Hemmen, 1992; Hoerzer, Legenstein,”
Macroscopic description for networks of spiking neurons (2015, background): “Firstly, in the past it has only been possible to calculate the approximate ﬁring rate of networks of spiking neurons for stationary states, or for weak deviations of such states [9– 12, 15, 20, 22].”
Stable Learning in Stochastic Network States (2012, background): “The network was set to an asynchronous irregular (AI) state (Brunel, 2000)withapopulationrateof (cid:13) 7spikes/sandwiththemeancoefficient of variation of the interspike interval (ISI CV) of 1.”
Synchronization from Second Order Network Connectivity Statistics (2011, background): “Previous studies have focused on synchronization of large networks with all-to-all connectivity (Hansel and Sompolinsky, 1992; Abbott and van Vreeswijk, 1993; Hansel et al., 1995) or independent random connectivity (Brunel, 2000).”
Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks (2004, methodology): “Using Fokker-Planck diﬀusion, the same kind of methods was used in the case of linear I&F neurons in (Fusi & Mattia, 1999; Mattia & Giudice, 2000), for stochastic networks dynamics with noisy input current (Giudice & Mattia, 2003) and in the case of sparse weight connectivity (Brunel, 2000).”
Role of local network oscillations in resting-state functional connectivity (2011, background): “In a number of experimental and theoretical studies (Bartos et al., 2007; Brunel, 2000; Brunel andWang, 2003; Borgers and Kopell, 2003), local neural networks have been shown to exhibit gamma oscillations, and the mechanisms leading to such oscillations are now widely accepted.”
Decorrelation of Neural-Network Activity by Inhibitory Feedback (2012, methodology): “In simulation experiments, we observed a gradual amplification of the population-rate fluctuations with increasing noise amplitude η (data not shown).”
Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons (2014, background): “where F is the current-to-rate transfer function of an LIF neuron receiving white noise, and, as in equations (9) and (10),”
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models (2015, background): “In some cases it is possible to describe the dynamics of LIF networks analytically [17,18], thus providing deeper insights into how spiking neuronal networks may implement the basic cerebral computational mechanisms [19].”
Cortical travelling waves: mechanisms and computational principles (2018, background): “However, it remains unknown whether and how spontaneous and stimulus-evoked waves can be generated in large-scale spiking networks with biologically realistic synaptic noise, in which model neurons exhibit the irregular asynchronous (IA) state that is the hallmark of cortical dynamics 111 .”
Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding (2010, background): “Second, rate propagation without accumulating correlations was observed when the background activity was asynchronous–irregular”
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input (2006, background): “tions of excitatory and inhibitory synaptic inputs is straightforward (Brunel 2000).”
Statistical mechanics of complex neural systems and high dimensional data (2013, methodology): “This work [134] led to four possible macroscopic phases of network dynamics, characterized by two possibilities for the temporal statistics of single neurons (regular periodic spike trains or irregular aperiodic spike trains) times two possibilities for the population average firing rates (synchrono”