Title: Self-sustained activity in balanced networks with low firing-rate,
Abstract: The brain can display self-sustained activity (SSA), which is the persistent
firing of neurons in the absence of external stimuli. This spontaneous activity
shows low neuronal firing rates and is observed in diverse in vitro and in vivo
situations. In this work, we study the influence of excitatory/inhibitory
balance, connection density, and network size on the self-sustained activity of
a neuronal network model. We build a random network of adaptive exponential
integrate-and-fire (AdEx) neuron models connected through inhibitory and
excitatory chemical synapses. The AdEx model mimics several behaviours of
biological neurons, such as spike initiation, adaptation, and bursting
patterns. In an excitation/inhibition balanced state, if the mean connection
degree (K) is fixed, the firing rate does not depend on the network size (N),
whereas for fixed N, the firing rate decreases when K increases. However, for
large K, SSA states can appear only for large N. We show the existence of SSA
states with similar behaviours to those observed in experimental recordings,
such as very low and irregular neuronal firing rates, and spike-train power
spectra with slow fluctuations, only for balanced networks of large size.