Analysis of neural excitability and oscillations
Methods in neuronal modeling
Reduction of conductance-based models with slow synapses to neural nets
Neural Computation
Synchrony in excitatory neural networks
Neural Computation
What matters in neuronal locking?
Neural Computation
On numerical simulations of integrate-and-fire neural networks
Neural Computation
Rate models for conductance-based cortical neuronal networks
Neural Computation
Synchrony in Heterogeneous Networks of Spiking Neurons
Neural Computation
Dynamic Brain - from Neural Spikes to Behaviors
Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
The properties and stability analysis of an integrate-and-fire model
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Journal of Computational Neuroscience
Phase response curves, delays and synchronization in MATLAB
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
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We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of integrate-and-fire neurons with spike adaptation. The dependence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparseness of the connectivity, and noise. In networks of two populations, one excitatory and one inhibitory, we show that decreasing the inhibitory feedback can cause the network to switch from a tonically active, asynchronous state to the synchronized bursting state. Finally, we show that the same mechanism also causes synchronized burst activity in networks of more realistic conductance-based model neurons.