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Self-organization and pattern formation in network-organized systems emergesfrom the collective activation and interaction of many interconnected units. Astriking feature of these non-equilibrium structures is that they are oftenlocalized and robust: only a small subset of the nodes, or cell assembly, isactivated. Understanding the role of cell assemblies as basic functional unitsin neural networks and socio-technical systems emerges as a fundamentalchallenge in network theory. A key open question is how these elementarybuilding blocks emerge, and how they operate, linking structure and function incomplex networks. Here we show that a network analogue of the Swift-Hohenbergcontinuum model---a minimal-ingredients model of nodal activation andinteraction within a complex network---is able to produce a complex suite oflocalized patterns. Hence, the spontaneous formation of robust operational cellassemblies in complex networks can be explained as the result ofself-organization, even in the absence of synaptic reinforcements. Our resultsshow that these self-organized, local structures can provide robust functionalunits to understand natural and socio-technical network-organized processes.

Self-organization of network dynamics into local quantized states