Gerstner W. (1995). Time structure of the activity in neural network models. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 51 [PubMed]

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References and models cited by this paper
References and models that cite this paper

Brunel N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience. 8 [PubMed]

Brunel N, Hakim V. (1999). Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural computation. 11 [PubMed]

Brzosko Z, Zannone S, Schultz W, Clopath C, Paulsen O. (2017). Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. eLife. 6 [PubMed]

Chow CC, Kopell N. (2000). Dynamics of spiking neurons with electrical coupling. Neural computation. 12 [PubMed]

Câteau H, Reyes AD. (2006). Relation between single neuron and population spiking statistics and effects on network activity. Physical review letters. 96 [PubMed]

Eggert J, van Hemmen JL. (2001). Modeling neuronal assemblies: theory and implementation. Neural computation. 13 [PubMed]

Gerstner W, Kistler WM. (2002). Mathematical formulations of Hebbian learning. Biological cybernetics. 87 [PubMed]

Herrmann A, Gerstner W. (2001). Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron. Journal of computational neuroscience. 11 [PubMed]

Herrmann A, Gerstner W. (2002). Noise and the PSTH response to current transients: II. Integrate-and-fire model with slow recovery and application to motoneuron data. Journal of computational neuroscience. 12 [PubMed]

Jolivet R, Lewis TJ, Gerstner W. (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. Journal of neurophysiology. 92 [PubMed]

Kistler WM, De Zeeuw CI. (2003). Time windows and reverberating loops: a reverse-engineering approach to cerebellar function. Cerebellum (London, England). 2 [PubMed]

Ly C, Tranchina D. (2007). Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling. Neural computation. 19 [PubMed]

Mongillo G, Amit DJ. (2001). Oscillations and irregular emission in networks of linear spiking neurons. Journal of computational neuroscience. 11 [PubMed]

Muller E, Buesing L, Schemmel J, Meier K. (2007). Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theories. Neural computation. 19 [PubMed]

Nykamp DQ, Tranchina D. (2000). A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning. Journal of computational neuroscience. 8 [PubMed]

Omurtag A, Knight BW, Sirovich L. (2000). On the simulation of large populations of neurons. Journal of computational neuroscience. 8 [PubMed]

Shelley M, McLaughlin D. (2002). Coarse-grained reduction and analysis of a network model of cortical response: I. Drifting grating stimuli. Journal of computational neuroscience. 12 [PubMed]

Soula H, Chow CC. (2007). Stochastic dynamics of a finite-size spiking neural network. Neural computation. 19 [PubMed]

Susin E, Destexhe A. (2021). Integration, coincidence detection and resonance in networks of spiking neurons expressing gamma oscillations and asynchronous states PLoS computational biology. 17 [PubMed]

Tino P, Mills AJ. (2006). Learning beyond finite memory in recurrent networks of spiking neurons. Neural computation. 18 [PubMed]

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