The neural criticality hypothesis states that the brain may be poised in a critical state at a
boundary between different types of dynamics. Theoretical and experimental studies show …

Adaptive networks appear in many biological applications. They combine topological
evolution of the network with dynamics in the network nodes. Recently, the dynamics of …

Depending on the connectivity, recurrent networks of simple computational units can show
very different types of dynamics, ranging from totally ordered to chaotic. We analyze how the …

Brain function depends on adaptive self-organization of large-scale neural assemblies, but
little is known about quantitative network parameters governing these processes in humans …

Self-organized criticality is an attractive model for human brain dynamics, but there has been
little direct evidence for its existence in large-scale systems measured by neuroimaging. In …

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 …

A long-standing hypothesis at the interface of physics and neuroscience is that neural
networks self-organize to the critical point of a phase transition, thereby optimizing aspects …

Social network structure has often been attributed to two network evolution mechanisms—
triadic closure and choice homophily—which are commonly considered independently or …

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 …

Critical dynamics are assumed to be an attractive mode for normal brain functioning as
information processing and computational capabilities are found to be optimal in the critical …