  A spontaneously active neural system that is capable of continual learning
should also be capable of homeostasis of both firing rate and connectivity.
Experimental evidence suggests that both types of homeostasis exist, and that
connectivity is maintained at a state that is optimal for information
transmission and storage. This state is referred to as the critical state. We
present a simple stochastic computational Hebbian learning model that
incorporates both firing rate and critical homeostasis, and we explore its
stability and connectivity properties. We also examine the behavior of our
model with a simulated seizure and with simulated acute deafferentation. We
argue that a neural system that is more highly connected than the critical
state (i.e., one that is "supercritical") is epileptogenic. Based on our
simulations, we predict that the post-seizural and post-deafferentation states
should be supercritical and epileptogenic. Furthermore, interventions that
boost spontaneous activity should be protective against epileptogenesis.
