  It is by now established that, remarkably, the addition of noise to a
nonlinear system may sometimes facilitate, rather than hamper the detection of
weak signals. This phenomenon, usually referred to as stochastic resonance, was
originally associated with strictly periodic signals, but it was eventually
shown to occur for stationary aperiodic signals as well. However, in several
situations of practical interest, the signal can be markedly nonstationary. We
demonstrate that the phenomenon of stochastic resonance extends to
nonstationary signals as well, and thus could be relevant to a wider class of
biological and electronic applications. Building on both nondynamic and
aperiodic stochastic resonance, our scheme is based on a multilevel trigger
mechanism, which could be realized as a parallel network of differentiated
threshold sensors. We find that optimal detection is reached for a number of
thresholds of order ten, and that little is gained by going much beyond that
number. We raise the question of whether this is related to the fact that
evolution has favored some fixed numbers of precisely this order of magnitude
in certain aspects of sensory perception.
