Cortical systems such as the visual cortex exhibit behaviors that change according to behavioral and stimulus context. 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 as a whole system 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.
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.
R
Cortical systems such as the visual cortex exhibit behaviors that
Experimental evidence suggests that the computational state of cortical systems
R
It
However, it
R
, which as a whole system
which