title: Self-organization of network dynamics into local quantized states.
abstract: Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of the Swift-Hohenberg continuum model-a minimal-ingredients model of nodal activation and interaction within a complex network-is able to produce a complex suite of localized patterns. Hence, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.
authors.affiliation: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Christos
authors.initials: C
authors.lastname: Nicolaides
authors.affiliation: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Ruben
authors.initials: R
authors.lastname: Juanes
authors.affiliation: Department of Hydraulics, Energy and Environment, Technical University of Madrid, Spain.|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Luis
authors.initials: L
authors.lastname: Cueto-Felgueroso
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer