Patterns of synchrony in neural networks with spike adaptation

We study the emergence of synchronized burst activity in networks of neuron s with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a sync hronized manner. The mechanism leading to this burst activity is analyzed i n a network of integrate-and-fire neurons with spike adaptation. The depend ence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparsen ess of the connectivity, and noise. In networks of two populations, one exc itatory and one inhibitory, we show that decreasing the inhibitory feedback can cause the network to switch from a tonically active, asynchronous stat e to the synchronized bursting state. Finally, we show that the same mechan ism also causes synchronized burst activity in networks of more realistic c onductance-based model neurons.