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
Weakly connected neural networks
Weakly connected neural networks
Synchrony in Heterogeneous Networks of Spiking Neurons
Neural Computation
Dynamics of Strongly Coupled Spiking Neurons
Neural Computation
Type i membranes, phase resetting curves, and synchrony
Neural Computation
IEEE Transactions on Neural Networks
Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Study on the role of GABAergic synapses in synchronization
Neurocomputing
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The dynamics of a pair of weakly interacting conductance-based neurons, firing at low frequency, ν, is investigated in the framework of the phase-reduction method. The stability of the antiphase and the in-phase locked state is studied. It is found that for a large class of conductance-based models, the antiphase state is stable (resp., unstable) for excitatory (resp., inhibitory) interactions if the synaptic time constant is above a critical value τsc, which scales as | log ν | when ν goes to zero.