title: Simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis.
abstract: 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 postseizural and postdeafferentation states should be supercritical and epileptogenic. Furthermore, interventions that boost spontaneous activity should be protective against epileptogenesis.
authors.affiliation: Department of Neurology, University of Wisconsin, Madison, Wisconsin 53792, USA. hsu@neurology.wisc.edu
authors.forename: David
authors.initials: D
authors.lastname: Hsu
authors.forename: Aonan
authors.initials: A
authors.lastname: Tang
authors.forename: Murielle
authors.initials: M
authors.lastname: Hsu
authors.forename: John M
authors.initials: JM
authors.lastname: Beggs
mesh_terms: D000465:Algorithms; D000818:Animals; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D004827:Epilepsy / Q000175:diagnosis*; D006706:Homeostasis; D006801:Humans; D007858:Learning; D008954:Models, Biological; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D009474:Neurons / Q000378:metabolism*; D016011:Normal Distribution; D011336:Probability; D013997:Time Factors