Self-sustained firing in populations of integrate-and-fire neurons
SIAM Journal on Applied Mathematics
Associative dynamics in a chaotic neural network
Neural Networks
Chaotic balanced state in a model of cortical circuits
Neural Computation
Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
Polychronization: Computation with Spikes
Neural Computation
The high-conductance state of cortical networks
Neural Computation
Simple model of spiking neurons
IEEE Transactions on Neural Networks
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In this paper, we study the self-sustained irregular firing activity in 2-D small-world (SW) neural networks consisting of both excitatory and inhibitory neurons by computational modeling. For a proper proportion of unidirectional shortcuts, the stable self-sustained activity with irregular firing states indeed occurs in the considered network. By varying the shortcut density while keeping other system parameters fixed, different levels of irregular firing states, from weakly irregular to Poisson-like and burst firing states, are obtained in 2-D SW neural networks. It is also observed that this activity is sensitive to small perturbations, which might provide a possible mechanism for producing chaos. On the other hand, we find that several other system parameters, such as the network size and refractory period, have significant impact on this activity. Further simulation results show that the 2-D SW neural network can sustain such long-lasting firing behavior by using a smaller number of connections than the random neural network.