Subthreshold stimulus encoding on a stochastic scale-free neuronal network

Random networks with complex topology arise in many different fields of science. Recently, it has been shown that existing network models fail to incorporate two common features of real networks in nature: First, real networks are open and continuously grow by addition of new elements, and second, a new element connects preferentially to an element that already has a large number of connections. Therefore, a new network model, called a scale-free (SF) network, has been proposed based on these two features. In this study, we study the subthreshold periodic stimulus encoding on a stochastic SF neuronal network based on the collective firing regularity. The network consists of identical Hodgkin-Huxley (HH) neurons. We show that the collective firing (spiking) regularity becomes maximal at a given stimulus frequency, corresponding to the frequency of the subthreshold oscillations of HH neurons. We also show that this best regularity can be obtained if the coupling strength and average degree of connectivity have their optimal values.