title: Self-organized criticality in developing neuronal networks.
abstract: Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.
authors.affiliation: Bernstein Center for Computational Neuroscience, Institute of Physics III - Biophysics, Georg-August Universität, Göttingen, Germany. tetzlaff@physik3.gwdg.de
authors.forename: Christian
authors.initials: C
authors.lastname: Tetzlaff
authors.forename: Samora
authors.initials: S
authors.lastname: Okujeni
authors.forename: Ulrich
authors.initials: U
authors.lastname: Egert
authors.forename: Florentin
authors.initials: F
authors.lastname: Wörgötter
authors.forename: Markus
authors.initials: M
authors.lastname: Butz
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology; D019295:Computational Biology; D003712:Dendrites; D006706:Homeostasis / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000254:growth & development / Q000502:physiology; D016501:Neurites; D009474:Neurons / Q000502:physiology*; D051381:Rats; D017208:Rats, Wistar; D013569:Synapses