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

• Sparsely connected networks of spiking neurons (Brunel 2000) [Model]

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

• Fast global oscillations in networks of I&F neurons with low firing rates (Brunel and Hakim 1999) [Model]

Brunel N, Hansel D. (2006). How noise affects the synchronization properties of recurrent networks of inhibitory neurons. Neural computation. 18 [PubMed]

Hansel D, Mato G. (2003). Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neurons. Neural computation. 15 [PubMed]

Jeong HY, Gutkin B. (2007). Synchrony of neuronal oscillations controlled by GABAergic reversal potentials. Neural computation. 19 [PubMed]

Kaltenbrunner A, Gómez V, López V. (2007). Phase transition and hysteresis in an ensemble of stochastic spiking neurons. Neural computation. 19 [PubMed]

Kanamaru T. (2006). Analysis of synchronization between two modules of pulse neural networks with excitatory and inhibitory connections. Neural computation. 18 [PubMed]

Kanamaru T, Sekine M. (2005). Synchronized firings in the networks of class 1 excitable neurons with excitatory and inhibitory connections and their dependences on the forms of interactions. Neural computation. 17 [PubMed]

Li YX, Wang YQ, Miura R. (2003). Clustering in small networks of excitatory neurons with heterogeneous coupling strengths. Journal of computational neuroscience. 14 [PubMed]

Nicola W, Campbell SA. (2013). Bifurcations of large networks of two-dimensional integrate and fire neurons. Journal of computational neuroscience. 35 [PubMed]

• Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013) [Model]

Tiesinga PH, Sejnowski TJ. (2004). Rapid temporal modulation of synchrony by competition in cortical interneuron networks. Neural computation. 16 [PubMed]

Tikidji-Hamburyan RA, Leonik CA, Canavier CC. (2019). Phase response theory explains cluster formation in sparsely but strongly connected inhibitory neural networks and effects of jitter due to sparse connectivity. Journal of neurophysiology. 121 [PubMed]

• Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019) [Model]