Parabolic bursting in an excitable system coupled with a slow oscillation
SIAM Journal on Applied Mathematics
Analysis of neural excitability and oscillations
Methods in neuronal modeling
Synchrony in excitatory neural networks
Neural Computation
What matters in neuronal locking?
Neural Computation
Dynamics of the firing probability of noisy integrate-and-fire neurons
Neural Computation
The Combined Effects of Inhibitory and Electrical Synapses in Synchrony
Neural Computation
Patterns of Synchrony in Neural Networks with Spike Adaptation
Neural Computation
Synchrony in Heterogeneous Networks of Spiking Neurons
Neural Computation
Phase Transition and Hysteresis in an Ensemble of Stochastic Spiking Neurons
Neural Computation
Stochastic dynamics of a finite-size spiking neural network
Neural Computation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Systematic fluctuation expansion for neural network activity equations
Neural Computation
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GABAergic interneurons play a major role in the emergence of various types of synchronous oscillatory patterns of activity in the central nervous system. Motivated by these experimental facts, modeling studies have investigated mechanisms for the emergence of coherent activity in networks of inhibitory neurons. However, most of these studies have focused either when the noise in the network is absent or weak or in the opposite situation when it is strong. Hence, a full picture of how noise affects the dynamics of such systems is still lacking. The aim of this letter is to provide a more comprehensive understanding of the mechanisms by which the asynchronous states in large, fully connected networks of inhibitory neurons are destabilized as a function of the noise level. Three types of single neuron models are considered: the leaky integrateand-fire (LIF) model, the exponential integrate-and-fire (EIF), model and conductance-based models involving sodium and potassium Hodgkin-Huxley (HH) currents. We show that in all models, the instabilities of the asynchronous state can be classified in two classes. The first one consists of clustering instabilities, which exist in a restricted range of noise. These instabilities lead to synchronous patterns in which the population of neurons is broken into clusters of synchronously firing neurons. The irregularity of the firing patterns of the neurons is weak. The second class of instabilities, termed oscillatory firing rate instabilities, exists at any value of noise. They lead to cluster state at low noise. As the noise is increased, the instability occurs at larger coupling, and the pattern of firing that emerges becomes more irregular. In the regime of high noise and strong coupling, these instabilities lead to stochastic oscillations in which neurons fire in an approximately Poisson way with a common instantaneous probability of firing that oscillates in time.