Parabolic bursting in an excitable system coupled with a slow oscillation
SIAM Journal on Applied Mathematics
Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
Synchrony in excitatory neural networks
Neural Computation
Weakly connected neural networks
Weakly connected neural networks
Neural networks with dynamic synapses
Neural Computation
Reading neuronal synchrony with depressing synapses
Neural Computation
Patterns of Synchrony in Neural Networks with Spike Adaptation
Neural Computation
Spike-timing error backpropagation in theta neuron networks
Neural Computation
Dependence of correlated firing on strength of inhibitory feedback
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Journal of Computational Neuroscience
Information filtering by synchronous spikes in a neural population
Journal of Computational Neuroscience
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Previous work has shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons can reveal oscillatory activity. For example, Börgers and Kopell (2003) have shown that oscillations occur when the excitatory neurons receive a sufficiently large input. A constant drive to the excitatory neurons is sufficient for oscillatory activity. Other studies (Doiron, Chacron, Maler, Longtin, & Bastian, 2003; Doiron, Lindner, Longtin, Maler, & Bastian, 2004) have shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons reveal oscillatory activity only if the excitatory neurons receive correlated input, regardless of the amount of excitatory input. In this study, we show that these apparently contradictory results can be explained by the behavior of a single model operating in different regimes of parameter space. Moreover, we show that adding dynamic synapses in the inhibitory feedback loop provides a robust network behavior over a broad range of stimulus intensities, contrary to that of previous models. A remarkable property of the introduction of dynamic synapses is that the activity of the network reveals synchronized oscillatory components in the case of correlated input, but also reflects the temporal behavior of the input signal to the excitatory neurons. This allows the network to encode both the temporal characteristics of the input and the presence of spatial correlations in the input simultaneously.