  Experimental evidence suggests that the computational state of cortical
systems change according to behavioral and stimulus context. However, it is
still unknown what mechanisms underlie this adaptive processing in cortical
circuitry. In this paper, we present a model of randomly coupled supercritical
Andronov-Hopf oscillators which can act as an adaptive processor by exhibiting
drastically different dynamics depending on the value of a single network
parameter. Despite being only composed of supercritical subunits, the full
system can exhibit either supercritical or subcritical Andronov-Hopf
bifurcations. This model might provide a novel mechanism for switching between
globally asymptotically stable and nonhyperbolic neural network types in
pattern recognition theory.
