  Catastrophic failures are complete and sudden collapses in the activity of
large networks such as economics, electrical power grids and computer networks,
which typically require a manual recovery process. Here we experimentally show
that excitatory neural networks are governed by a non-Poissonian reoccurrence
of catastrophic failures, where their repetition time follows a multimodal
distribution characterized by a few tenths of a second and tens of seconds
timescales. The mechanism underlying the termination and reappearance of
network activity is quantitatively shown here to be associated with nodal
time-dependent features, neuronal plasticity, where hyperactive nodes damage
the response capability of their neighbors. It presents a complementary
mechanism for the emergence of Poissonian catastrophic failures from damage
conductivity. The effect that hyperactive nodes degenerate their neighbors
represents a type of local competition which is a common feature in the
dynamics of real-world complex networks, whereas their spontaneous recoveries
represent a vitality which enhances reliable functionality.
