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
Chaotic balanced state in a model of cortical circuits
Neural Computation
Type i membranes, phase resetting curves, and synchrony
Neural Computation
Contour Detection by Synchronization of Integrate-and-Fire Neurons
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Stimulus Competition by Inhibitory Interference
Neural Computation
Patterns of Synchrony in Neural Networks with Spike Adaptation
Neural Computation
On Synchrony of Weakly Coupled Neurons at Low Firing Rate
Neural Computation
Synchronization of the Neural Response to Noisy Periodic Synaptic Input
Neural Computation
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The emergence of synchrony in the activity of large, heterogeneous networks of spiking neurons is investigated. We define the robustness of synchrony by the critical disorder at which the asynchronous state becomes linearly unstable. We show that at low firing rates, synchrony is more robust in excitatory networks than in inhibitory networks, but excitatory networks cannot display any synchrony when the average firing rate becomes too high. We introduce a new regime where all inputs, external and internal, are strong and have opposite effects that cancel each other when averaged. In this regime, the robustness of synchrony is strongly enhanced, and robust synchrony can be achieved at a high firing rate in inhibitory networks. On the other hand, in excitatory networks, synchrony remains limited in frequency due to the intrinsic instability of strong recurrent excitation.