  The brain forms and stores distributed representations from sparse external
input that compete for neuronal resources with already stored memory traces. It
is unclear what dynamical properties of neural systems allow formation and
subsequent consolidation of new, distributed memory representations under these
conditions. Here we use analytical, computational, and experimental approaches
to show that a dynamical regime near a phase-transition in neuronal network
activity (i.e. criticality) may play an important role in this process. Our
results reveal that near-critical dynamics are necessary to stabilize and store
new sparsely driven representations when they compete with native network
states.
