Dynamics of a recurrent network of spiking neurons before and following learning

By D. Amit, Nicolas J.-B. Brunel. 1997

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “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,…”
Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks (2004, background): “In the particular case of integrate and ﬁre neurons, (Amit & Brunel, 1997a, 1997b) used consistency techniques on nets of irregular ﬁring neurons.”
A working memory model based on fast Hebbian learning (2003, background): “In contrast to network models of long-term memory, current working memor ym odels rely mainly on reverberatory, localized persistent activity [ 2 , 9, 18] in a network with fixed connectivity.”
Computing and Stability in Cortical Networks (2004, methodology): “¡° .º E // C f 1Á E .º E ; m/: (2.5b) These equations are similar to ones derived previously by Brunel and colleagues (Amit & Brunel, 1997a; Brunel, 2000; Brunel & Wang, 2001).”
Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons (2000, background): “Amit and Brunel (1997a) showed, both by simulations and by mean-field analysis, that the main effect of variations of the number of inputs from cell to cell in the stationary, inhibition-dominated regime is to give rise to very wide spatial distributions of firing rates among neurons.”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, background): “These include the study of the non-equilibrium dynamics of networks (Prete and Coolen 2004), dynamic attractors of the sort hypothesized to provide a model of olfactory processing (Laurent 2002), and syn-ﬁre chains (Abeles 1991; Diesmann et al. 1996; Câteau and Fukai 2001).”
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions (2006, methodology): “This approach has been used to analytically study spiking neuronal network models comprised of integrate-and-fire types (Amit and Brunel, 1997a,b; Brunel and Wang, 2001; Renart et al., 2003).”