title: Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain.
abstract: Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.
authors.affiliation: University of Colorado at Boulder, Boulder, Colorado 80309, USA.
authors.forename: Yogesh S
authors.initials: YS
authors.lastname: Virkar
authors.affiliation: University of Arkansas, Fayetteville, Arkansas 72701, USA.
authors.forename: Woodrow L
authors.initials: WL
authors.lastname: Shew
authors.affiliation: University of Colorado at Boulder, Boulder, Colorado 80309-0526, USA.
authors.forename: Juan G
authors.initials: JG
authors.lastname: Restrepo
authors.affiliation: University of Maryland, College Park, Maryland 20742, USA.
authors.forename: Edward
authors.initials: E
authors.lastname: Ott
mesh_terms: D000200:Action Potentials; D001921:Brain / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009457:Neuroglia / Q000502:physiology*; D009473:Neuronal Plasticity; D013569:Synapses / Q000502:physiology