We present a detailed analysis of the dynamical regimes observed in a balanced network of
identical quadratic integrate-and-fire neurons with sparse connectivity for homogeneous and …

We extensively explore networks of weakly unbalanced, leaky integrate-and-fire (LIF)
neurons for different coupling strength, connectivity, and by varying the degree of …

We present a simple model for coherent, spatially correlated chaos in a recurrent neural
network. Networks of randomly connected neurons exhibit chaotic fluctuations and have …

We revisit the dynamics of a prototypical model of balanced activity in networks of spiking
neurons. A detailed investigation of the thermodynamic limit for fixed density of connections …

Firing patterns in the central nervous system often exhibit strong temporal irregularity and
considerable heterogeneity in time-averaged response properties. Previous studies …

We study the dynamical stability of pulse coupled networks of leaky integrate-and-fire
neurons against infinitesimal and finite perturbations. In particular, we compare mean versus …

We investigate a discrete-time network model composed of excitatory and inhibitory neurons
and dynamic synapses with the aim at revealing dynamical properties behind oscillatory …

Networks of fast nonlinear elements may display slow fluctuations if interactions are strong.
We find a transition in the long-term variability of a sparse recurrent network of perfect …

Macroscopic variability is an emergent property of neural networks, typically manifested in
spontaneous switching between the episodes of elevated neuronal activity and the …

Irregular dynamics in multidimensional systems is commonly associated with chaos. For
infinitely large sparse networks of spiking neurons, mean field theory shows that a balanced …