title: Critical branching captures activity in living neural networks and maximizes the number of metastable States.
abstract: Recent experimental work has shown that activity in living neural networks can propagate as a critical branching process that revisits many metastable states. Neural network theory suggests that attracting states could store information, but little is known about how a branching process could form such states. Here we use a branching process to model actual data and to explore metastable states in the network. When we tune the branching parameter to the critical point, we find that metastable states are most numerous and that network dynamics are not attracting, but neutral.
authors.affiliation: Department of Physics, Indiana University, Bloomington, Indiana, USA.
authors.forename: Clayton
authors.initials: C
authors.lastname: Haldeman
authors.forename: John M
authors.initials: JM
authors.lastname: Beggs
mesh_terms: D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*