title: Self-organized criticality and scale-free properties in emergent functional neural networks.
abstract: Recent studies on complex systems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks. We show that the functional structures in the brain can be self-organized to both small-world and scale-free networks by synaptic reorganization via spike timing dependent synaptic plasticity instead of conventional Hebbian learning rules. We show that the balance between the excitatory and the inhibitory synaptic inputs is critical in the formation of the functional structure, which is found to lie in a self-organized critical state.
authors.affiliation: National Core Research Center for System Biodynamics, and Nonlinear and Complex Systems Laboratory, Department of Physics, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Gyungbuk, Korea, 790-784. shine@postech.ac.kr
authors.forename: Chang-Woo
authors.initials: CW
authors.lastname: Shin
authors.forename: Seunghwan
authors.initials: S
authors.lastname: Kim
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*