  Self-sustained activity in the brain is observed in the absence of external
stimuli and contributes to signal propagation, neural coding, and dynamic
stability. It also plays an important role in cognitive processes. In this
work, by means of studying intracellular recordings from CA1 neurons in rats
and results from numerical simulations, we demonstrate that self-sustained
activity presents high variability of patterns, such as low neural firing rates
and activity in the form of small-bursts in distinct neurons. In our numerical
simulations, we consider random networks composed of coupled, adaptive
exponential integrate-and-fire neurons. The neural dynamics in the random
networks simulate regular spiking (excitatory) and fast-spiking (inhibitory)
neurons. We show that both the connection probability and network size are
fundamental properties that give rise to self-sustained activity in qualitative
agreement with our experimental results. Finally, we provide a more detailed
description of the self-sustained activity in terms of lifetime distributions,
synaptic conductances, and synaptic currents.
