  Cortical networks can maintain memories for decades despite the short
lifetime of synaptic strength. Can a neural network store long-lasting memories
in unstable synapses? Here, we study the effects of random noise on the
stability of memory stored in synapses of an attractor neural network. The
model includes ongoing spike timing dependent plasticity (STDP). We show that
certain classes of STDP rules can lead to the stabilization of memory patterns
stored in the network. The stabilization results from rehearsals induced by
noise. We show that unstructured neural noise, after passing through the
recurrent network weights, carries the imprint of all memory patterns in
temporal correlations. Under certain conditions, STDP combined with these
correlations, can lead to reinforcement of all existing patterns, even those
that are never explicitly visited. Thus, unstructured neural noise can
stabilize the existing structure of synaptic connectivity. Our findings may
provide the functional reason for highly irregular spiking displayed by
cortical neurons and provide justification for models of system memory
consolidation. Therefore, we propose that irregular neural activity is the
feature that helps cortical networks maintain stable connections.
