  The dynamical mechanism underlying the processes of anesthesia-induced loss
of consciousness and recovery is key to gaining insights into the working of
the nervous system. Previous experiments revealed an asymmetry between neural
signals during the anesthesia and recovery processes. Here we obtain
experimental evidence for the hysteresis loop and articulate the dynamical
mechanism based on percolation on multilayer complex networks with
self-similarity. Model analysis reveals that, during anesthesia, the network is
able to maintain its neural pathways despite the loss of a substantial fraction
of the edges. A predictive and potentially testable result is that, in the
forward process of anesthesia, the average shortest path and the clustering
coefficient of the neural network are markedly smaller than those associated
with the recovery process. This suggests that the network strives to maintain
certain neurological functions by adapting to a relatively more compact
structure in response to anesthesia.
