Stimulus-evoked activity in clustered networks of stochastic rate-based neurons

Understanding the effect of network connectivity patterns on the relation between the spontaneous and the stimulus-evoked network activity has become one of the outstanding issues in neuroscience. We address this problem by considering a clustered network of stochastic rate-based neurons influenced by external and intrinsic noise. The bifurcation analysis of an effective model of network dynamics, comprised of coupled mean-field models representing each of the clusters, is used to gain insight into the structure of metastable states characterizing the spontaneous and the induced dynamics. We show that the induced dynamics strongly depends on whether the excitation is aimed at a certain cluster or the same fraction of randomly selected units, whereby the targeted stimulation reduces macroscopic variability by biasing the network toward a particular collective state. The immediate effect of clustering on the induced dynamics is established by comparing the excitation rates of a clustered and a homogeneous random network